Tootfinder

Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
Zhengjia Wang, Qiang Sheng, Danding Wang, Beizhe Hu, Juan Cao
https://arxiv.org/abs/2509.01660

Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
Fake news detection is an important and challenging task for defending online information integrity. Existing state-of-the-art approaches typically extract news semantic clues, such as writing patterns that include emotional words, stylistic features, etc. However, detectors tuned solely to such semantic clues can easily fall into surface detection patterns, which can shift rapidly in dynamic environments, leading to limited performance in the evolving news landscape. To address this issue, thi…

LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Qianrui Zhou, Hua Xu, Yifan Wang, Xinzhi Dong, Hanlei Zhang
https://arxiv.org/abs/2509.01337 https://

LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Understanding human intents from multimodal signals is critical for analyzing human behaviors and enhancing human-machine interactions in real-world scenarios. However, existing methods exhibit limitations in their modality-level reliance, constraining relational reasoning over fine-grained semantics for complex intent understanding. This paper proposes a novel LLM-Guided Semantic Relational Reasoning (LGSRR) method, which harnesses the expansive knowledge of large language models (LLMs) to est…

After a lifetime of scandal,
Trump expresses doubt that he will be admitted to paradise.
But he appears increasingly intent on finding other paths to eternity.
https://www.nytimes.com/2025/10/30/us/poli

Trump’s Search for Eternity: Heaven? Maybe Not, He Says. Monuments? Absolutely.
After a lifetime of scandal, the president expresses doubt that he will be admitted to paradise. But he appears increasingly intent on finding other paths to eternity.

"Refinery fires are part of life in the South Bay, which is home to several major oil production facilities." A statement suggesting maybe #FossilFools aren't competent to manage things safely. (Of course, their on the #ClimateCrisis seem more born of malicious intent, rather than …

"Refinery fires are part of life in the South Bay, which is home to several major oil production facilities." A statement suggesting maybe #FossilFools aren't competent to manage things safely. (Of course, their on the #ClimateCrisis seem more born of malicious intent, rather than …

LogPilot: Intent-aware and Scalable Alert Diagnosis for Large-scale Online Service Systems
Zhihan Jiang, Jinyang Liu, Yichen Li, Haiyu Huang, Xiao He, Tieying Zhang, Jianjun Chen, Yi Li, Rui Shi, Michael R. Lyu
https://arxiv.org/abs/2509.25874

LogPilot: Intent-aware and Scalable Alert Diagnosis for Large-scale Online Service Systems
Effective alert diagnosis is essential for ensuring the reliability of large-scale online service systems. However, on-call engineers are often burdened with manually inspecting massive volumes of logs to identify root causes. While various automated tools have been proposed, they struggle in practice due to alert-agnostic log scoping and the inability to organize complex data effectively for reasoning. To overcome these limitations, we introduce LogPilot, an intent-aware and scalable framework…

Judging by Appearances? Auditing and Intervening Vision-Language Models for Bail Prediction
Sagnik Basu, Shubham Prakash, Ashish Maruti Barge, Siddharth D Jaiswal, Abhisek Dash, Saptarshi Ghosh, Animesh Mukherjee
https://arxiv.org/abs/2510.00088

Judging by Appearances? Auditing and Intervening Vision-Language Models for Bail Prediction
Large language models (LLMs) have been extensively used for legal judgment prediction tasks based on case reports and crime history. However, with a surge in the availability of large vision language models (VLMs), legal judgment prediction systems can now be made to leverage the images of the criminals in addition to the textual case reports/crime history. Applications built in this way could lead to inadvertent consequences and be used with malicious intent. In this work, we run an audit to i…

Cowboys Willing to Trade Ex-First Round Pick Before Deadline: Insider https://heavy.com/sports/nfl/dallas-cowboys/mazi-smith-available-trade/?adt_ei=[email]

Designing a Lightweight GenAI Interface for Visual Data Analysis
Ratanond Koonchanok, Alex Kale, Khairi Reda
https://arxiv.org/abs/2509.02878 https://arxiv…

Designing a Lightweight GenAI Interface for Visual Data Analysis
Recent advances in Generative AI have transformed how users interact with data analysis through natural language interfaces. However, many systems rely too heavily on LLMs, creating risks of hallucination, opaque reasoning, and reduced user control. We present a hybrid visual analysis system that integrates GenAI in a constrained, high-level role to support statistical modeling while preserving transparency and user agency. GenAI translates natural language intent into formal statistical formul…

Strata-Sword: A Hierarchical Safety Evaluation towards LLMs based on Reasoning Complexity of Jailbreak Instructions
Shiji Zhao, Ranjie Duan, Jiexi Liu, Xiaojun Jia, Fengxiang Wang, Cheng Wei, Ruoxi Cheng, Yong Xie, Chang Liu, Qing Guo, Jialing Tao, Hui Xue, Xingxing Wei
https://arxiv.org/abs/2509.01444…

Strata-Sword: A Hierarchical Safety Evaluation towards LLMs based on Reasoning Complexity of Jailbreak Instructions
Large language models (LLMs) have gained widespread recognition for their superior comprehension and have been deployed across numerous domains. Building on Chain-of-Thought (CoT) ideology, Large Reasoning models (LRMs) further exhibit strong reasoning skills, enabling them to infer user intent more accurately and respond appropriately. However, both LLMs and LRMs face the potential safety risks under jailbreak attacks, which raise concerns about their safety capabilities. Current safety evalua…

Sam Altman signs a letter of intent with Samsung and SK Hynix to supply Stargate; Samsung and SK Hynix say demand from OpenAI could hit 900K wafers per month (Yoolim Lee/Bloomberg)
https://www.bloomberg.com/news/articles/2025-1…

Central Path Art
Thor Catteau, Benjamin Glancy, Allen Holder, Angela Milkowski, Alexa Renner, Connor Tasik, Rebecca Testa
https://arxiv.org/abs/2510.01933 https://

Central Path Art
The central path revolutionized the study of optimization in the 1980s and 1990s due to its favorable convergence properties, and as such, it has been investigated analytically, algorithmically, and computationally. Past pursuits have primarily focused on linking iterative approximation algorithms to the central path in the design of efficient algorithms to solve large, and sometimes novel, optimization problems. This algorithmic intent has meant that the central path has rarely been celebrated…

I like progressive psytrance, symphonic death metal and avant-garde metal, the kind of noise that reminds me nothing matters and everything still moves in color.
Zoë Straub, Shadow of Intent, Avalon, Tristan, and Mad Tribe stay in the rotation because they actually sound like something real.
My #Bandcamp:

Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
Anis Bekri, Amar Abane, Abdella Battou, Saddek Bensalem
https://arxiv.org/abs/2509.22834 htt…

Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
Intent-Based Networking (IBN) aims to simplify network management by enabling users to specify high-level goals that drive automated network design and configuration. However, translating informal natural-language intents into formally correct optical network topologies remains challenging due to inherent ambiguity and lack of rigor in Large Language Models (LLMs). To address this, we propose a novel hybrid pipeline that integrates LLM-based intent parsing, formal methods, and Optical Retrieval…

Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?
JiaYing Zheng, HaiNan Zhang, Liang Pang, YongXin Tong, ZhiMing Zheng
https://arxiv.org/abs/2509.22325

Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?
Multi-turn RAG systems often face queries with colloquial omissions and ambiguous references, posing significant challenges for effective retrieval and generation. Traditional query rewriting relies on human annotators to clarify queries, but due to limitations in annotators' expressive ability and depth of understanding, manually rewritten queries often diverge from those needed in real-world RAG systems, resulting in a gap between user intent and system response. We observe that high-quality …

The problem with "you can just do things" is that other people can also just do things. You can get into an arms race of just doing things, or learn how to build shared intent.
One of those approaches is masculine and exciting. The other one actually works.
Guess which one AI-powered design & development tools encourage?

The social lives of ideas (Don't Just Do Things, Part 3)
No amount of outputs will help maintain unbroken intent all the way through to delivery.

MINT-RVAE: Multi-Cues Intention Prediction of Human-Robot Interaction using Human Pose and Emotion Information from RGB-only Camera Data
Farida Mohsen, Ali Safa
https://arxiv.org/abs/2509.22573

MINT-RVAE: Multi-Cues Intention Prediction of Human-Robot Interaction using Human Pose and Emotion Information from RGB-only Camera Data
Efficiently detecting human intent to interact with ubiquitous robots is crucial for effective human-robot interaction (HRI) and collaboration. Over the past decade, deep learning has gained traction in this field, with most existing approaches relying on multimodal inputs, such as RGB combined with depth (RGB-D), to classify time-sequence windows of sensory data as interactive or non-interactive. In contrast, we propose a novel RGB-only pipeline for predicting human interaction intent with fra…

Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links
Selen Gecgel Cetin, Tolga Ovatman, Gunes Karabulut Kurt
https://arxiv.org/abs/2508.16314

Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links
This letter addresses essential aspects of threat assessment by proposing intent-driven threat models that incorporate both capabilities and intents. We propose a holistic framework for cyber physical awareness (CPA) in space networks, pointing out that analyzing reliability and security separately can lead to overfitting on system-specific criteria. We structure our proposed framework in three main steps. First, we suggest an algorithm that extracts characteristic properties of the received si…

Mitigating Jailbreaks with Intent-Aware LLMs
Wei Jie Yeo, Ranjan Satapathy, Erik Cambria
https://arxiv.org/abs/2508.12072 https://arxiv.org/pdf/2508.12072

Mitigating Jailbreaks with Intent-Aware LLMs
Despite extensive safety-tuning, large language models (LLMs) remain vulnerable to jailbreak attacks via adversarially crafted instructions, reflecting a persistent trade-off between safety and task performance. In this work, we propose Intent-FT, a simple and lightweight fine-tuning approach that explicitly trains LLMs to infer the underlying intent of an instruction before responding. By fine-tuning on a targeted set of adversarial instructions, Intent-FT enables LLMs to generalize intent ded…

Smart Contract Intent Detection with Pre-trained Programming Language Model
Youwei Huang, Jianwen Li, Sen Fang, Yao Li, Peng Yang, Bin Hu, Tao Zhang
https://arxiv.org/abs/2508.20086

Smart Contract Intent Detection with Pre-trained Programming Language Model
Malicious intent in smart contract development can lead to substantial economic losses. SmartIntentNN is a deep learning model specifically designed to identify unsafe intents in smart contracts. This model integrates the Universal Sentence Encoder, a K-means clustering-based intent highlighting mechanism, and a Bidirectional Long Short-Term Memory network for multi-label classification, achieving an F1 of 0.8633 in distinguishing ten different intent categories. In this study, we present an up…

SQUARE: Semantic Query-Augmented Fusion and Efficient Batch Reranking for Training-free Zero-Shot Composed Image Retrieval
Ren-Di Wu, Yu-Yen Lin, Huei-Fang Yang
https://arxiv.org/abs/2509.26330

SQUARE: Semantic Query-Augmented Fusion and Efficient Batch Reranking for Training-free Zero-Shot Composed Image Retrieval
Composed Image Retrieval (CIR) aims to retrieve target images that preserve the visual content of a reference image while incorporating user-specified textual modifications. Training-free zero-shot CIR (ZS-CIR) approaches, which require no task-specific training or labeled data, are highly desirable, yet accurately capturing user intent remains challenging. In this paper, we present SQUARE, a novel two-stage training-free framework that leverages Multimodal Large Language Models (MLLMs) to enha…

SIG-Chat: Spatial Intent-Guided Conversational Gesture Generation Involving How, When and Where
Yiheng Huang, Junran Peng, Silei Shen, Jingwei Yang, ZeJi Wei, ChenCheng Bai, Yonghao He, Wei Sui, Muyi Sun, Yan Liu, Xu-Cheng Yin, Man Zhang, Zhaoxiang Zhang, Chuanchen Luo
https://arxiv.org/abs/2509.23852

SIG-Chat: Spatial Intent-Guided Conversational Gesture Generation Involving How, When and Where
The accompanying actions and gestures in dialogue are often closely linked to interactions with the environment, such as looking toward the interlocutor or using gestures to point to the described target at appropriate moments. Speech and semantics guide the production of gestures by determining their timing (WHEN) and style (HOW), while the spatial locations of interactive objects dictate their directional execution (WHERE). Existing approaches either rely solely on descriptive language to gen…

I am very disappointed in #MayorLurie's action re TNC's on Market. It's clear the voters got snookered/[played by the language re "commercial vehicles" - where the clear intent was to allow deliveries to the businesses on Market. Conveniently ignoring the commercial licenses of TNCs - but hey, we need to keep the segment that can afford to use the TNCs happy, don't we…

Lurie is damaging transit, the environment—and democracy—on Market Street - 48 hills
The mayor is undoing the will of the voters and opening the street to Uber, Lyft, Waymo—and others are sure to follow

I am very disappointed in #MayorLurie's action re TNC's on Market. It's clear the voters got snookered/[played by the language re "commercial vehicles" - where the clear intent was to allow deliveries to the businesses on Market. Conveniently ignoring the commercial licenses of TNCs - but hey, we need to keep the segment that can afford to use the TNCs happy, don't we…

Lurie is damaging transit, the environment—and democracy—on Market Street - 48 hills
The mayor is undoing the will of the voters and opening the street to Uber, Lyft, Waymo—and others are sure to follow

Sense of Belonging and Intent to Persist: Mediating Role of Motivation and Moderating Role of Gender in Physics and Astronomy Graduate Students
Swagata Sarkar, N. Sanjay Rebello
https://arxiv.org/abs/2508.20948

Sense of Belonging and Intent to Persist: Mediating Role of Motivation and Moderating Role of Gender in Physics and Astronomy Graduate Students
This study investigates how graduate students' sense of belonging (SB) influences their intent to persist (IP) in physics and astronomy programs, and how this relationship is shaped by the basic psychological needs that drive motivation-autonomy, competence, and relatedness-as well as gender. Grounded in self-determination theory, the analysis treats these three needs as mediators and gender as a moderator. A quantitative survey was administered to graduate students in the Department of Physics…

Members of Israeli government have ‘genocidal intent’, says UK minister
Good.
But at this rate the UK government will say it's a genocide just as the last Palestinian is expelled.
https://nation.cymru/news/members-of-israeli-gover…

Day 5: Robin Wall Kimmerer
I'm taking these liberty of changing my hashtag and expanding the intent of this list to include all non-men, although Kimerer is a woman so I'll get to more gender diversity later... I've also started planning this out more and realized that I may continue a bit beyond 20...
In any case, Robin Wall Kimmerer is an Indigenous academic biologist and excellent non-fiction author whose work touches on Potawotomi philosophy, colonialism (including in academic spaces), and ideas for a better future. Anyone interested in ecology, conservation, or decolonization in North America will probably be impressed by her work and the rich connections she weaves between academic ecology and Indigenous knowledge offer a critical opportunity to expand your understanding of the world if like me you were raised deeply enmeshed in "Western" scientific tradition. I suppose a little background in skepticism helped prepare me to respect her writing, but I don't think that's essential.
I've only read "Braiding Sweetgrass," but "Gathering Moss" and her more recent "The Serviceberry" are high on my to-read list, despite my predilection for fiction. Kimmerer incorporates a backbone of fascinating anecdotes into "Braiding Sweetgrass" that makes it surprisingly easy reading for a work that's philosophical at its core. She also pulls off an impressive braided organization to the whole thing, weaving together disparate knowledges in a way that lets you see both their contradictions and their connections.
The one criticism I've seen of her work is that it's not sufficiently connected to other Indigenous philosophers & writers, and that it's perhaps too comfortable of a read for colonizers, and that seems valid to me, even though (perhaps because I am a colonizer) I still find her book important.
An excellent author in any case, and one doing concrete ideological work towards a better world.
#20AuthorsNoMen

Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection
Ankan Mullick, Saransh Sharma, Abhik Jana, Pawan Goyal
https://arxiv.org/abs/2508.16122 https://

Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection
The rise of multimodal data, integrating text, audio, and visuals, has created new opportunities for studying multimodal tasks such as intent detection. This work investigates the effectiveness of Large Language Models (LLMs) and non-LLMs, including text-only and multi-modal models, in the multimodal intent detection task. Our study reveals that Mistral-7B, a text-only LLM, outperforms most competitive multimodal models by approximately 9% on MIntRec-1 and 4% on MIntRec2.0 datasets. This perfor…

The comments are *overwhelmingly* anti-Bezos, and many express their intent to cancel subscriptions (I access through a library subscription, canceled my own).
Opinion | Trump replacing the White House East Wing is not the end of the world - The Washington Post
https://wapo.st/4o2FcGU

IntentionReasoner: Facilitating Adaptive LLM Safeguards through Intent Reasoning and Selective Query Refinement
Yuanzhe Shen, Zisu Huang, Zhengkang Guo, Yide Liu, Guanxu Chen, Ruicheng Yin, Xiaoqing Zheng, Xuanjing Huang
https://arxiv.org/abs/2508.20151

IntentionReasoner: Facilitating Adaptive LLM Safeguards through Intent Reasoning and Selective Query Refinement
The rapid advancement of large language models (LLMs) has driven their adoption across diverse domains, yet their ability to generate harmful content poses significant safety challenges. While extensive research has focused on mitigating harmful outputs, such efforts often come at the cost of excessively rejecting harmless prompts. Striking a balance among safety, over-refusal, and utility remains a critical challenge. In this work, we introduce IntentionReasoner, a novel safeguard mechanism th…

Ah, the smell of #desperation / #enshittification - one washed up shit-i-fied social media platform (#Reddit) begging for users/advertisers/relevance on _another_ shit-i-fied social media platform (

"5 Tricks to Make Your Run Faster, Healthier and More Fun"
The grumpy old man in me hates the clickbait-y title, but it's an otherwise nice overview of different types of runs (tempo, strides, progression, etc.) and their intent. I wish I had something like this when I was starting out.

5 Tricks to Make Your Run Faster, Healthier and More Fun
Speed work isn’t just for serious runners. Up your pace just a little for a more efficient workout.

Mitigating Category Imbalance: Fosafer System for the Multimodal Emotion and Intent Joint Understanding Challenge
Honghong Wang, Yankai Wang, Dejun Zhang, Jing Deng, Rong Zheng
https://arxiv.org/abs/2508.11362

Mitigating Category Imbalance: Fosafer System for the Multimodal Emotion and Intent Joint Understanding Challenge
This paper presents Fosafer approach to the Track 2 Mandarin in the Multimodal Emotion and Intent Joint Understandingchallenge, which focuses on achieving joint recognition of emotion and intent in Mandarin, despite the issue of category imbalance. To alleviate this issue, we use a variety of data augmentation techniques across text, video, and audio modalities. Additionally, we introduce the SampleWeighted Focal Contrastive loss, designed to address the challenges of recognizing minority class…

PromptSleuth: Detecting Prompt Injection via Semantic Intent Invariance
Mengxiao Wang, Yuxuan Zhang, Guofei Gu
https://arxiv.org/abs/2508.20890 https://arx…

PromptSleuth: Detecting Prompt Injection via Semantic Intent Invariance
Large Language Models (LLMs) are increasingly integrated into real-world applications, from virtual assistants to autonomous agents. However, their flexibility also introduces new attack vectors-particularly Prompt Injection (PI), where adversaries manipulate model behavior through crafted inputs. As attackers continuously evolve with paraphrased, obfuscated, and even multi-task injection strategies, existing benchmarks are no longer sufficient to capture the full spectrum of emerging threats. …

I'm pretty sure the Fash intent is to Just Let The Poor Starve starting next week.
I hope to hell that I'm wrong.

Bridging Developer Instructions and Code Completion Through Instruction-Aware Fill-in-the-Middle Paradigm
Zhensu Sun, Chengran Yang, Chao Peng, Pengfei Gao, Xiaoning Du, Li Li, David Lo
https://arxiv.org/abs/2509.24637

Bridging Developer Instructions and Code Completion Through Instruction-Aware Fill-in-the-Middle Paradigm
Large Language Models (LLMs) have significantly advanced code completion, yet they often fail when the developer's intent is underspecified in the code context. To address this, developers usually add natural language instructions (e.g., comments) into the code context to clarify their intent. However, existing code LLMs applied for code completion systems merely undergo a fill-in-the-middle (FIM) pre-training, which struggles to leverage this information effectively due to the lack of instruct…

Reagan's "Welfare Queen" rhetoric was a dog whistle. The whole "southern strategy" was full of dog whistles. Every time Republicans talked about gay marriage as a "states rights" issue, those were dog whistles. Naming a government agency "the Department of Homeland Security" was a dog whistle. Trump's speeches leading up to his first term were full of dog whistles.
Nazis have total control of all branches of government. They've cut off funding to all but the most oppressive elements of the government. There are concentration camps, both in client states (CECOT, among others) and in the US (Alligator Auschwitz, among others). They're actively carrying out ethnic cleansing.
When DHS puts out some Nazi shit, it's not a dog whistle. It's a vuvuzela. They're not trying to signal their intent without any of the "normies" noticing. They actively doing what they wanted to do. It's not a signal because there's nothing to signal. It's a celebration.
#USPol

"Die einstigen "digiatalstrategischen" Planungen der CDU"
@… im @…
„Ziel des damals geplanten Joint Venture, so Bröcker unter Verweis auf einen ihm vorliegenden Letter of Intent, sei es unter an…

Has anyone making this feature actually asked any accessibility experts and any disabled people if this is a good idea?
Because in practice this will give you terrible results, because AI can’t know what you intent do communicate with the image.
Even if it would give good results (it doesn’t and never will) and you’re too lazy to write alt text yourself, it would be better to do this on the receiving end where the disabled person could fine-tune generative alt text to their specific needs.
https://mastodon.social/@MonaApp/115406286404369048

Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition
Guangyu Lei, Tianhao Liang, Yuqi Ping, Xinglin Chen, Longyu Zhou, Junwei Wu, Xiyuan Zhang, Huahao Ding, Xingjian Zhang, Weijie Yuan, Tingting Zhang, Qinyu Zhang
https://arxiv.org/abs/2509.06312

Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition
The rapid development of the low-altitude economy emphasizes the critical need for effective perception and intent recognition of non-cooperative unmanned aerial vehicles (UAVs). The advanced generative reasoning capabilities of multimodal large language models (MLLMs) present a promising approach in such tasks. In this paper, we focus on the combination of UAV intent recognition and the MLLMs. Specifically, we first present an MLLM-enabled UAV intent recognition architecture, where the multimo…

"Where does it hurt?" - Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues
Tom R\"ohr, Soumyadeep Roy, Fares Al Mohamad, Jens-Michalis Papaioannou, Wolfgang Nejdl, Felix Gers, Alexander L\"oser
https://arxiv.org/abs/2508.19077

"Where does it hurt?" - Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues
In a doctor-patient dialogue, the primary objective of physicians is to diagnose patients and propose a treatment plan. Medical doctors guide these conversations through targeted questioning to efficiently gather the information required to provide the best possible outcomes for patients. To the best of our knowledge, this is the first work that studies physician intent trajectories in doctor-patient dialogues. We use the `Ambient Clinical Intelligence Benchmark' (Aci-bench) dataset for our stu…

Trump’s MAGA agenda is increasingly at odds with free market economics, and some key conservatives are beginning to notice.
At least one Republican—Texas Representative Chip Roy—has harpooned the president’s Intel deal, reminding CNBC Thursday that government stakes in private entities defies conservative values.
Roy also challenged the Trump administration’s intent to develop a state-owned investment fund known as a "sovereign wealth fund".
“I think the problem he…

MAGA Rep. Slams Trump’s Shady Takeover of Businesses
The Texas Republican has sparred with him in the past.

A look at SiriusXM's podcasting business; 2024's "off-platform earnings" were $606M, with podcast advertising making up most of that sum, up from $475M in 2022 (Jessica Testa/New York Times)
https://www.nytimes.com/2025/08/22/busines

Podcasting Is a Talent Business. SiriusXM Is Intent on Winning.
The company famous for car radio and Howard Stern has recently climbed the podcast charts by betting hundreds of millions of dollars on shows like “Call Her Daddy” and “SmartLess.”

Prompting Robot Teams with Natural Language
Nicolas Pfitzer, Eduardo Sebasti\'an, Ajay Shankar, Amanda Prorok
https://arxiv.org/abs/2509.24575 https://…

Prompting Robot Teams with Natural Language
This paper presents a framework towards prompting multi-robot teams with high-level tasks using natural language expressions. Our objective is to use the reasoning capabilities demonstrated by recent language models in understanding and decomposing human expressions of intent, and repurpose these for multi-robot collaboration and decision-making. The key challenge is that an individual's behavior in a collective can be hard to specify and interpret, and must continuously adapt to actions from o…

Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline
Haiyang Li, Yaxiong Wang, Lianwei Wu, Lechao Cheng, Zhun Zhong
https://arxiv.org/abs/2509.25991

Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline
In recent years, detecting fake multimodal content on social media has drawn increasing attention. Two major forms of deception dominate: human-crafted misinformation (e.g., rumors and misleading posts) and AI-generated content produced by image synthesis models or vision-language models (VLMs). Although both share deceptive intent, they are typically studied in isolation. NLP research focuses on human-written misinformation, while the CV community targets AI-generated artifacts. As a result, e…

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Peigen Ye, Jingpu Duan, Hongyang Du, Yulan Guo
https://arxiv.org/abs/2508.05884 https://

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Semantic communication focuses on transmitting task-relevant semantic information, aiming for intent-oriented communication. While existing systems improve efficiency by extracting key semantics, they still fail to deeply understand and generalize users' real intentions. To overcome this, we propose a user-intention-driven semantic communication system that interprets diverse abstract intents. First, we integrate a multi-modal large model as semantic knowledge base to generate user-intention pr…

This is a public service announcement for all the dollar-store Sherlock Holmes’s out there who clearly have nothing better to do than to concoct conspiracy theories about people from Gaza on the fediverse who are faced with genocide and famine:
Stop.
Have some humanity.
Case in point, do not do what Daniel (@helmet91@mastodon.social) is doing here:

Mouse-Guided Gaze: Semi-Supervised Learning of Intention-Aware Representations for Reading Detection
Seongsil Heo, Roberto Manduchi
https://arxiv.org/abs/2509.19574 https://

Mouse-Guided Gaze: Semi-Supervised Learning of Intention-Aware Representations for Reading Detection
Understanding user intent during magnified reading is critical for accessible interface design. Yet magnification collapses visual context and forces continual viewport dragging, producing fragmented, noisy gaze and obscuring reading intent. We present a semi-supervised framework that learns intention-aware gaze representations by leveraging mouse trajectories as weak supervision. The model is first pretrained to predict mouse velocity from unlabeled gaze, then fine-tuned to classify reading ve…

Trump, in his address to the UN, claimed that the people who came up with the "carbon footprint" did so with evil intent.
Mens rea aside, there might actually be something to that?
It was a BP advertising campaign.

A CVE should be assigned whenever an attacker publishes a malicious package update to a distribution, it’s a released software artifact with malicious intent and must be tracked.

How good are LLMs at Retrieving Documents in a Specific Domain?
Nafis Tanveer Islam, Zhiming Zhao
https://arxiv.org/abs/2509.22658 https://arxiv.org/pdf/25…

How good are LLMs at Retrieving Documents in a Specific Domain?
Classical search engines using indexing methods in data infrastructures primarily allow keyword-based queries to retrieve content. While these indexing-based methods are highly scalable and efficient, due to a lack of an appropriate evaluation dataset and a limited understanding of semantics, they often fail to capture the user's intent and generate incomplete responses during evaluation. This problem also extends to domain-specific search systems that utilize a Knowledge Base (KB) to access da…

A Novel Integrated Architecture for Intent Based Approach and Zero Touch Networks
Neelam Gupta, Dibakar Das, Tamizhelakkiya K, Uma Maheswari Natarajan, Sharvari Ravindran, Komal Sharma, Jyotsna Bapat, Debabrata Das
https://arxiv.org/abs/2509.21026

A Novel Integrated Architecture for Intent Based Approach and Zero Touch Networks
The transition to Sixth Generation (6G) networks presents challenges in managing quality of service (QoS) of diverse applications and achieving Service Level Agreements (SLAs) under varying network conditions. Hence, network management must be automated with the help of Machine Learning (ML) and Artificial Intelligence (AI) to achieve real-time requirements. Zero touch network (ZTN) is one of the frameworks to automate network management with mechanisms such as closed loop control to ensure tha…

AgentPack: A Dataset of Code Changes, Co-Authored by Agents and Humans
Yangtian Zi, Zixuan Wu, Aleksander Boruch-Gruszecki, Jonathan Bell, Arjun Guha
https://arxiv.org/abs/2509.21891

AgentPack: A Dataset of Code Changes, Co-Authored by Agents and Humans
Fine-tuning large language models for code editing has typically relied on mining commits and pull requests. The working hypothesis has been that commit messages describe human intent in natural language, and patches to code describe the changes that implement that intent. However, much of the previously collected data is noisy: commit messages are terse, human-written commits commingle several unrelated edits, and many commits come from simple, rule-based bots. The recent adoption of softwar…

Adversarial Distilled Retrieval-Augmented Guarding Model for Online Malicious Intent Detection
Yihao Guo, Haocheng Bian, Liutong Zhou, Ze Wang, Zhaoyi Zhang, Francois Kawala, Milan Dean, Ian Fischer, Yuantao Peng, Noyan Tokgozoglu, Ivan Barrientos, Riyaaz Shaik, Rachel Li, Chandru Venkataraman, Reza Shifteh Far, Moses Pawar, Venkat Sundaranatha, Michael Xu, Frank Chu

Adversarial Distilled Retrieval-Augmented Guarding Model for Online Malicious Intent Detection
With the deployment of Large Language Models (LLMs) in interactive applications, online malicious intent detection has become increasingly critical. However, existing approaches fall short of handling diverse and complex user queries in real time. To address these challenges, we introduce ADRAG (Adversarial Distilled Retrieval-Augmented Guard), a two-stage framework for robust and efficient online malicious intent detection. In the training stage, a high-capacity teacher model is trained on adv…

SafeBehavior: Simulating Human-Like Multistage Reasoning to Mitigate Jailbreak Attacks in Large Language Models
Qinjian Zhao, Jiaqi Wang, Zhiqiang Gao, Zhihao Dou, Belal Abuhaija, Kaizhu Huang
https://arxiv.org/abs/2509.26345

SafeBehavior: Simulating Human-Like Multistage Reasoning to Mitigate Jailbreak Attacks in Large Language Models
Large Language Models (LLMs) have achieved impressive performance across diverse natural language processing tasks, but their growing power also amplifies potential risks such as jailbreak attacks that circumvent built-in safety mechanisms. Existing defenses including input paraphrasing, multi step evaluation, and safety expert models often suffer from high computational costs, limited generalization, or rigid workflows that fail to detect subtle malicious intent embedded in complex contexts. I…

Court filing: Elon Musk says he talked to Mark Zuckerberg in February about backing a $97.4B OpenAI bid, but neither he nor Meta signed a letter of intent (Financial Times)
https://www.ft.com/content/12494c21-e477-49c1-a2d7-683c12c281ea

Active Query Selection for Crowd-Based Reinforcement Learning
Jonathan Erskine, Taku Yamagata, Ra\'ul Santos-Rodr\'iguez
https://arxiv.org/abs/2508.19132 https://…

Active Query Selection for Crowd-Based Reinforcement Learning
Preference-based reinforcement learning has gained prominence as a strategy for training agents in environments where the reward signal is difficult to specify or misaligned with human intent. However, its effectiveness is often limited by the high cost and low availability of reliable human input, especially in domains where expert feedback is scarce or errors are costly. To address this, we propose a novel framework that combines two complementary strategies: probabilistic crowd modelling to …

Robot Trajectron V2: A Probabilistic Shared Control Framework for Navigation
Pinhao Song, Yurui Du, Ophelie Saussus, Sofie De Schrijver, Irene Caprara, Peter Janssen, Renaud Detry
https://arxiv.org/abs/2509.19954

Robot Trajectron V2: A Probabilistic Shared Control Framework for Navigation
We propose a probabilistic shared-control solution for navigation, called Robot Trajectron V2 (RT-V2), that enables accurate intent prediction and safe, effective assistance in human-robot interaction. RT-V2 jointly models a user's long-term behavioral patterns and their noisy, low-dimensional control signals by combining a prior intent model with a posterior update that accounts for real-time user input and environmental context. The prior captures the multimodal and history-dependent nature o…

OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation
Jianwen Jiang, Weihong Zeng, Zerong Zheng, Jiaqi Yang, Chao Liang, Wang Liao, Han Liang, Yuan Zhang, Mingyuan Gao
https://arxiv.org/abs/2508.19209

OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation
Existing video avatar models can produce fluid human animations, yet they struggle to move beyond mere physical likeness to capture a character's authentic essence. Their motions typically synchronize with low-level cues like audio rhythm, lacking a deeper semantic understanding of emotion, intent, or context. To bridge this gap, \textbf{we propose a framework designed to generate character animations that are not only physically plausible but also semantically coherent and expressive.} Our mod…

SemanticShield: LLM-Powered Audits Expose Shilling Attacks in Recommender Systems
Kaihong Li, Huichi Zhou, Bin Ma, Fangjun Huang
https://arxiv.org/abs/2509.24961 https://…

SemanticShield: LLM-Powered Audits Expose Shilling Attacks in Recommender Systems
Recommender systems (RS) are widely used in e-commerce for personalized suggestions, yet their openness makes them susceptible to shilling attacks, where adversaries inject fake behaviors to manipulate recommendations. Most existing defenses emphasize user-side behaviors while overlooking item-side features such as titles and descriptions that can expose malicious intent. To address this gap, we propose a two-stage detection framework that integrates item-side semantics via large language model…

From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation
KaiWen Wei, Kejun He, Xiaomian Kang, Jie Zhang, Yuming Yang, Jiang Zhong, He Bai, Junnan Zhu
https://arxiv.org/abs/2509.23649

From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation
Generative recommendation, which directly generates item identifiers, has emerged as a promising paradigm for recommendation systems. However, its potential is fundamentally constrained by the reliance on purely autoregressive training. This approach focuses solely on predicting the next item while ignoring the rich internal structure of a user's interaction history, thus failing to grasp the underlying intent. To address this limitation, we propose Masked History Learning (MHL), a novel traini…

There's a type of guy whose only contribution at work is sheer volume of outputs, whether or not they serve any purpose. #AI tools ask us, "what if everyone could be that guy?"
It turns out that the result is bad for everyone. Systems lose their ability to evaluate whether outputs are fit for purpose. Shared intent disappears.
Scaling up trash only makes more trash. Work tha…

All your problems are people problems (Don't Just Do Things, Part 2)
If you want to do strategy, you must be willing to do process transformation first.

“I would suggest the intent is to ensure that any individual person who’s acting under color of law or as an agent of the state has as much leeway to act out their personal bigotry as they want, without any concern about consequences”
DHS Orders Airlines To Disregard "X" Passport Markers - Joe.My.God.
https://www.joemygod.com/2025/10/dhs-orders-airlines-to-disregard-x-passport-markers/

The DOJ says it will no longer target developers who create decentralized crypto platforms without criminal intent, as part of a regulatory retreat on crypto (Chris Prentice/Reuters)
https://www.reuters.com/sustainability/boa…

Tactile-Based Human Intent Recognition for Robot Assistive Navigation
Shaoting Peng, Dakarai Crowder, Wenzhen Yuan, Katherine Driggs-Campbell
https://arxiv.org/abs/2509.16353 ht…

Tactile-Based Human Intent Recognition for Robot Assistive Navigation
Robot assistive navigation (RAN) is critical for enhancing the mobility and independence of the growing population of mobility-impaired individuals. However, existing systems often rely on interfaces that fail to replicate the intuitive and efficient physical communication observed between a person and a human caregiver, limiting their effectiveness. In this paper, we introduce Tac-Nav, a RAN system that leverages a cylindrical tactile skin mounted on a Stretch 3 mobile manipulator to provide a…

Shadow of Intent - The Facets of Propaganda (Lyric Video) (HQ)
"Through the hands of the blind
The sightless open the gates
Meticulously evolving
The facets of propaganda"
https://www.youtube.com/watch?v=ZBju1BZ95fI

Human-Centered Design for Connected Automation: Predicting Pedestrian Crossing Intentions
Sanaz Motamedi, Viktoria Marcus, Griffin Pitts
https://arxiv.org/abs/2508.20464 https:/…

Human-Centered Design for Connected Automation: Predicting Pedestrian Crossing Intentions
Road traffic remains a leading cause of death worldwide, with pedestrians and other vulnerable road users accounting for over half of the 1.19 million annual fatalities, much of it due to human error. Level-5 automated driving systems (ADSs), capable of full self-driving without human oversight, have the potential to reduce these incidents. However, their effectiveness depends not only on automation performance but also on their ability to communicate intent and coordinate safely with pedestria…

Performance Evaluation of Intent-Based Networking Scenarios: A GitOps and Nephio Approach
Saptarshi Ghosh, Ioannis Mavromatis, Konstantinos Antonakoglou, Konstantinos Katsaros
https://arxiv.org/abs/2509.13901

Performance Evaluation of Intent-Based Networking Scenarios: A GitOps and Nephio Approach
GitOps has emerged as a foundational paradigm for managing cloud-native infrastructures by enabling declarative configuration, version-controlled state, and automated reconciliation between intents and runtime deployments. Despite its widespread adoption, the performance and scalability of GitOps tools in Intent-Based Networking (IBN) scenarios are insufficiently evaluated. This paper presents a reproducible, metric-driven benchmarking, assessing the latency and resource overheads of three wide…

Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
Qihang Zhao, Xiaoyang Zheng, Ben Chen, Zhongbo Sun, Chenyi Lei
https://arxiv.org/abs/2509.19876

Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
User behavior sequences in search systems resemble "interest fossils", capturing genuine intent yet eroded by exposure bias, category drift, and contextual noise. Current methods predominantly follow an "identify-aggregate" paradigm, assuming sequences immutably reflect user preferences while overlooking the organic entanglement of noise and genuine interest. Moreover, they output static, context-agnostic representations, failing to adapt to dynamic intent shifts under varying Query-User-Item-C…

Pixel Motion Diffusion is What We Need for Robot Control
E-Ro Nguyen, Yichi Zhang, Kanchana Ranasinghe, Xiang Li, Michael S. Ryoo
https://arxiv.org/abs/2509.22652 https://

Pixel Motion Diffusion is What We Need for Robot Control
We present DAWN (Diffusion is All We Need for robot control), a unified diffusion-based framework for language-conditioned robotic manipulation that bridges high-level motion intent and low-level robot action via structured pixel motion representation. In DAWN, both the high-level and low-level controllers are modeled as diffusion processes, yielding a fully trainable, end-to-end system with interpretable intermediate motion abstractions. DAWN achieves state-of-the-art results on the challengin…

Multi-Intent Recognition in Dialogue Understanding: A Comparison Between Smaller Open-Source LLMs
Adnan Ahmad, Philine Kowol, Stefan Hillmann, Sebastian M\"oller
https://arxiv.org/abs/2509.10010

Multi-Intent Recognition in Dialogue Understanding: A Comparison Between Smaller Open-Source LLMs
In this paper, we provide an extensive analysis of multi-label intent classification using Large Language Models (LLMs) that are open-source, publicly available, and can be run in consumer hardware. We use the MultiWOZ 2.1 dataset, a benchmark in the dialogue system domain, to investigate the efficacy of three popular open-source pre-trained LLMs, namely LLama2-7B-hf, Mistral-7B-v0.1, and Yi-6B. We perform the classification task in a few-shot setup, giving 20 examples in the prompt with some i…

'Evidence of Genocidal Intent': Israeli General Calls for Killing 50 Palestinians for Every Oct. 7 Victim | Common Dreams
https://www.commondreams.org/news/is-israel-comitting-genocide

Agentic TinyML for Intent-aware Handover in 6G Wireless Networks
Alaa Saleh, Roberto Morabito, Sasu Tarkoma, Anders Lindgren, Susanna Pirttikangas, Lauri Lov\'en
https://arxiv.org/abs/2508.09147

Agentic TinyML for Intent-aware Handover in 6G Wireless Networks
As 6G networks evolve into increasingly AI-driven, user-centric ecosystems, traditional reactive handover mechanisms demonstrate limitations, especially in mobile edge computing and autonomous agent-based service scenarios. This manuscript introduces WAAN, a cross-layer framework that enables intent-aware and proactive handovers by embedding lightweight TinyML agents as autonomous, negotiation-capable entities across heterogeneous edge nodes that contribute to intent propagation and network ada…

Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction
Mansi Sharma, Shuang Chen, Philipp M\"uller, Maurice Rekrut, Antonio Kr\"uger
https://arxiv.org/abs/2508.01860

Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction
For machines to effectively assist humans in challenging visual search tasks, they must differentiate whether a human is simply glancing into a scene (navigational intent) or searching for a target object (informational intent). Previous research proposed combining electroencephalography (EEG) and eye-tracking measurements to recognize such search intents implicitly, i.e., without explicit user input. However, the applicability of these approaches to real-world scenarios suffers from two key li…

Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems
Wei Xu, Jiasen Zheng, Junjiang Lin, Mingxuan Han, Junliang Du
https://arxiv.org/abs/2509.04694

Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems
This paper addresses the challenge of jointly modeling user intent diversity and behavioral uncertainty in recommender systems. A unified representation learning framework is proposed. The framework builds a multi-intent representation module and an uncertainty modeling mechanism. It extracts multi-granularity interest structures from user behavior sequences. Behavioral ambiguity and preference fluctuation are captured using Bayesian distribution modeling. In the multi-intent modeling part, the…

Adaptive Motion Planning via Contact-Based Intent Inference for Human-Robot Collaboration
Jiurun Song, Xiao Liang, Minghui Zheng
https://arxiv.org/abs/2510.08811 https://…

Adaptive Motion Planning via Contact-Based Intent Inference for Human-Robot Collaboration
Human-robot collaboration (HRC) requires robots to adapt their motions to human intent to ensure safe and efficient cooperation in shared spaces. Although large language models (LLMs) provide high-level reasoning for inferring human intent, their application to reliable motion planning in HRC remains challenging. Physical human-robot interaction (pHRI) is intuitive but often relies on continuous kinesthetic guidance, which imposes burdens on operators. To address these challenges, a contact-inf…

Agentic JWT: A Secure Delegation Protocol for Autonomous AI Agents
Abhishek Goswami
https://arxiv.org/abs/2509.13597 https://arxiv.org/pdf/2509.13597

Agentic JWT: A Secure Delegation Protocol for Autonomous AI Agents
Autonomous LLM agents can issue thousands of API calls per hour without human oversight. OAuth 2.0 assumes deterministic clients, but in agentic settings stochastic reasoning, prompt injection, or multi-agent orchestration can silently expand privileges. We introduce Agentic JWT (A-JWT), a dual-faceted intent token that binds each agent's action to verifiable user intent and, optionally, to a specific workflow step. A-JWT carries an agent's identity as a one-way checksum hash derived from its…

Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
Cesar Alan Contreras, Manolis Chiou, Alireza Rastegarpanah, Michal Szulik, Rustam Stolkin
https://arxiv.org/abs/2508.11093

Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
Human-robot collaboration requires robots to quickly infer user intent, provide transparent reasoning, and assist users in achieving their goals. Our recent work introduced GUIDER, our framework for inferring navigation and manipulation intents. We propose augmenting GUIDER with a vision-language model (VLM) and a text-only language model (LLM) to form a semantic prior that filters objects and locations based on the mission prompt. A vision pipeline (YOLO for object detection and the Segment An…

GPT-5's router directs queries based on complexity and intent, helping OpenAI allocate compute for low-value informational and high-value commercial requests (SemiAnalysis)
https://semianalysis.com/2025/08/13/gpt-5-ad-monetization-and-the-superapp/

GPT-5 Set the Stage for Ad Monetization and the SuperApp
To many power users (Pro and Plus), GPT5 was a disappointing release. But with closer inspection, the real release is focused on the vast majority of ChatGPT’s users, which is the 700m+ free userba…

ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation
Daniel Lee, Nikhil Sharma, Donghoon Shin, DaEun Choi, Harsh Sharma, Jeonghwan Kim, Heng Ji
https://arxiv.org/abs/2508.06065

ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation
Generative AI has made image creation more accessible, yet aligning outputs with nuanced creative intent remains challenging, particularly for non-experts. Existing tools often require users to externalize ideas through prompts or references, limiting fluid exploration. We introduce ThematicPlane, a system that enables users to navigate and manipulate high-level semantic concepts (e.g., mood, style, or narrative tone) within an interactive thematic design plane. This interface bridges the gap b…

Large Language Model Data Generation for Enhanced Intent Recognition in German Speech
Theresa Pekarek Rosin, Burak Can Kaplan, Stefan Wermter
https://arxiv.org/abs/2508.06277 ht…

Large Language Model Data Generation for Enhanced Intent Recognition in German Speech
Intent recognition (IR) for speech commands is essential for artificial intelligence (AI) assistant systems; however, most existing approaches are limited to short commands and are predominantly developed for English. This paper addresses these limitations by focusing on IR from speech by elderly German speakers. We propose a novel approach that combines an adapted Whisper ASR model, fine-tuned on elderly German speech (SVC-de), with Transformer-based language models trained on synthetic text d…

Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Wen-Han Hsieh, Elvis Hsieh, Dantong Niu, Trevor Darrell, Roei Herzig, David M. Chan
https://arxiv.org/abs/2508.16292

Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role -- not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions: natural language commands that reference objects or condit…

From Watch to Imagine: Steering Long-horizon Manipulation via Human Demonstration and Future Envisionment
Ke Ye, Jiaming Zhou, Yuanfeng Qiu, Jiayi Liu, Shihui Zhou, Kun-Yu Lin, Junwei Liang
https://arxiv.org/abs/2509.22205

From Watch to Imagine: Steering Long-horizon Manipulation via Human Demonstration and Future Envisionment
Generalizing to long-horizon manipulation tasks in a zero-shot setting remains a central challenge in robotics. Current multimodal foundation based approaches, despite their capabilities, typically fail to decompose high-level commands into executable action sequences from static visual input alone. To address this challenge, we introduce Super-Mimic, a hierarchical framework that enables zero-shot robotic imitation by directly inferring procedural intent from unscripted human demonstration vid…

SemSR: Semantics aware robust Session-based Recommendations
Jyoti Narwariya, Priyanka Gupta, Muskan Gupta, Jyotsana Khatri, Lovekesh Vig
https://arxiv.org/abs/2508.20587 https:/…

SemSR: Semantics aware robust Session-based Recommendations
Session-based recommendation (SR) models aim to recommend items to anonymous users based on their behavior during the current session. While various SR models in the literature utilize item sequences to predict the next item, they often fail to leverage semantic information from item titles or descriptions impeding session intent identification and interpretability. Recent research has explored Large Language Models (LLMs) as promising approaches to enhance session-based recommendations, with b…

CapTune: Adapting Non-Speech Captions With Anchored Generative Models
Jeremy Zhengqi Huang, Calu\~a de Lacerda Pataca, Liang-Yuan Wu, Dhruv Jain
https://arxiv.org/abs/2508.19971

CapTune: Adapting Non-Speech Captions With Anchored Generative Models
Non-speech captions are essential to the video experience of deaf and hard of hearing (DHH) viewers, yet conventional approaches often overlook the diversity of their preferences. We present CapTune, a system that enables customization of non-speech captions based on DHH viewers' needs while preserving creator intent. CapTune allows caption authors to define safe transformation spaces using concrete examples and empowers viewers to personalize captions across four dimensions: level of detail, e…

CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation
Bo Xiong, Linghao Zhang, Chong Wang, Peng Liang
https://arxiv.org/abs/2509.18337 https://

CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation
Commit messages play a key role in documenting the intent behind code changes. However, they are often low-quality, vague, or incomplete, limiting their usefulness. Commit Message Generation (CMG) aims to automatically generate descriptive commit messages from code diffs to reduce developers' effort and improve message quality. Although recent advances in LLMs have shown promise in automating CMG, their performance remains limited. This paper aims to enhance CMG performance by retrieving simila…

Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant
Inbal Bolshinsky, Shani Kupiec, Almog Sasson, Yehudit Aperstein, Alexander Apartsin
https://arxiv.org/abs/2509.05006

Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant
In the era of conversational AI, generating accurate and contextually appropriate service responses remains a critical challenge. A central question remains: Is explicit intent recognition a prerequisite for generating high-quality service responses, or can models bypass this step and produce effective replies directly? This paper conducts a rigorous comparative study to address this fundamental design dilemma. Leveraging two publicly available service interaction datasets, we benchmark several…

Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Wen-Han Hsieh, Elvis Hsieh, Dantong Niu, Trevor Darrell, Roei Herzig, David M. Chan
https://arxiv.org/abs/2508.16292

Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role -- not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions: natural language commands that reference objects or condit…

SpecifyUI: Supporting Iterative UI Design Intent Expression through Structured Specifications and Generative AI
Yunnong Chen, Chengwei Shi, Liuqing Chen
https://arxiv.org/abs/2509.07334

SpecifyUI: Supporting Iterative UI Design Intent Expression through Structured Specifications and Generative AI
Large language models (LLMs) promise to accelerate UI design, yet current tools struggle with two fundamentals: externalizing designers' intent and controlling iterative change. We introduce SPEC, a structured, parameterized, hierarchical intermediate representation that exposes UI elements as controllable parameters. Building on SPEC, we present SpecifyUI, an interactive system that extracts SPEC from UI references via region segmentation and vision-language models, composes UIs across multipl…

Controlling Intent Expressiveness in Robot Motion with Diffusion Models
Wenli Shi, Clemence Grislain, Olivier Sigaud, Mohamed Chetouani
https://arxiv.org/abs/2510.12370 https://…

Controlling Intent Expressiveness in Robot Motion with Diffusion Models
Legibility of robot motion is critical in human-robot interaction, as it allows humans to quickly infer a robot's intended goal. Although traditional trajectory generation methods typically prioritize efficiency, they often fail to make the robot's intentions clear to humans. Meanwhile, existing approaches to legible motion usually produce only a single "most legible" trajectory, overlooking the need to modulate intent expressiveness in different contexts. In this work, we propose a novel motio…

Role-Augmented Intent-Driven Generative Search Engine Optimization
Xiaolu Chen, Haojie Wu, Jie Bao, Zhen Chen, Yong Liao, Hu Huang
https://arxiv.org/abs/2508.11158 https://

Role-Augmented Intent-Driven Generative Search Engine Optimization
Generative Search Engines (GSEs), powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), are reshaping information retrieval. While commercial systems (e.g., BingChat, Perplexity.ai) demonstrate impressive semantic synthesis capabilities, their black-box nature fundamentally undermines established Search Engine Optimization (SEO) practices. Content creators face a critical challenge: their optimization strategies, effective in traditional search engines, are misaligne…

AutoIntent: AutoML for Text Classification
Ilya Alekseev, Roman Solomatin, Darina Rustamova, Denis Kuznetsov
https://arxiv.org/abs/2509.21138 https://arxiv…

AutoIntent: AutoML for Text Classification
AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and …

Orchid: Orchestrating Context Across Creative Workflows with Generative AI
Srishti Palani, Gonzalo Ramos
https://arxiv.org/abs/2508.19517 https://arxiv.org…

Orchid: Orchestrating Context Across Creative Workflows with Generative AI
Context is critical for meaningful interactions between people and Generative AI (GenAI). Yet mainstream tools offer limited means to orchestrate it, particularly across workflows that span multiple interactions, sessions, and models, as often occurs in creative projects. Re specifying prior details, juggling diverse artifacts, and dealing with context drift overwhelm users, obscure intent, and curtail creativity. To address these challenges, we present Orchid, a system that gives its users aff…

LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings
Benjamin F. Maier, Ulf Aslak, Luca Fiaschi, Nina Rismal, Kemble Fletcher, Christian C. Luhmann, Robbie Dow, Kli Pappas, Thomas V. Wiecki
https://arxiv.org/abs/2510.08338

LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings
Consumer research costs companies billions annually yet suffers from panel biases and limited scale. Large language models (LLMs) offer an alternative by simulating synthetic consumers, but produce unrealistic response distributions when asked directly for numerical ratings. We present semantic similarity rating (SSR), a method that elicits textual responses from LLMs and maps these to Likert distributions using embedding similarity to reference statements. Testing on an extensive dataset compr…

WolBanking77: Wolof Banking Speech Intent Classification Dataset
Abdou Karim Kandji, Fr\'ed\'eric Precioso, Cheikh Ba, Samba Ndiaye, Augustin Ndione
https://arxiv.org/abs/2509.19271

DESAMO: A Device for Elder-Friendly Smart Homes Powered by Embedded LLM with Audio Modality
Youngwon Choi, Donghyuk Jung, Hwayeon Kim
https://arxiv.org/abs/2508.18918 https://…

DESAMO: A Device for Elder-Friendly Smart Homes Powered by Embedded LLM with Audio Modality
We present DESAMO, an on-device smart home system for elder-friendly use powered by Audio LLM, that supports natural and private interactions. While conventional voice assistants rely on ASR-based pipelines or ASR-LLM cascades, often struggling with the unclear speech common among elderly users and unable to handle non-speech audio, DESAMO leverages an Audio LLM to process raw audio input directly, enabling a robust understanding of user intent and critical events, such as falls or calls for he…

CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency
Zhanming Shen, Hao Chen, Yulei Tang, Shaolin Zhu, Wentao Ye, Xiaomeng Hu, Haobo Wang, Gang Chen, Junbo Zhao
https://arxiv.org/abs/2508.16100

CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency
Instruction tuning is vital for aligning large language models (LLMs) with human intent, but current methods typically rely on costly human-annotated seed data or powerful external teacher models. While instruction back-translation techniques reduce this dependency, they remain fundamentally tethered to an initial seed set, which limits full automation, introduces biases, and can lead to inefficient use of unlabeled corpora. In this paper, we propose Cycle-Instruct, a novel framework that achie…

A Semantic-Aware Framework for Safe and Intent-Integrative Assistance in Upper-Limb Exoskeletons
Yu Chen, Shu Miao, Chunyu Wu, Jingsong Mu, Bo OuYang, Xiang Li
https://arxiv.org/abs/2508.10378

A Semantic-Aware Framework for Safe and Intent-Integrative Assistance in Upper-Limb Exoskeletons
Upper-limb exoskeletons are primarily designed to provide assistive support by accurately interpreting and responding to human intentions. In home-care scenarios, exoskeletons are expected to adapt their assistive configurations based on the semantic information of the task, adjusting appropriately in accordance with the nature of the object being manipulated. However, existing solutions often lack the ability to understand task semantics or collaboratively plan actions with the user, limiting …

An Embodied AR Navigation Agent: Integrating BIM with Retrieval-Augmented Generation for Language Guidance
Hsuan-Kung Yang, Tsu-Ching Hsiao, Ryoichiro Oka, Ryuya Nishino, Satoko Tofukuji, Norimasa Kobori
https://arxiv.org/abs/2508.16602

An Embodied AR Navigation Agent: Integrating BIM with Retrieval-Augmented Generation for Language Guidance
Delivering intelligent and adaptive navigation assistance in augmented reality (AR) requires more than visual cues, as it demands systems capable of interpreting flexible user intent and reasoning over both spatial and semantic context. Prior AR navigation systems often rely on rigid input schemes or predefined commands, which limit the utility of rich building data and hinder natural interaction. In this work, we propose an embodied AR navigation system that integrates Building Information Mod…

TASC: Task-Aware Shared Control for Teleoperated Manipulation
Ze Fu, Pinhao Song, Yutong Hu, Renaud Detry
https://arxiv.org/abs/2509.10416 https://arxiv.or…

TASC: Task-Aware Shared Control for Teleoperated Manipulation
We present TASC, a Task-Aware Shared Control framework for teleoperated manipulation that infers task-level user intent and provides assistance throughout the task. To support everyday tasks without predefined knowledge, TASC constructs an open-vocabulary interaction graph from visual input to represent functional object relationships, and infers user intent accordingly. A shared control policy then provides rotation assistance during both grasping and object interaction, guided by spatial cons…