Tootfinder

Source: OpenAI is preparing ChatGPT agents to let users create files compatible with PowerPoint and Excel, generate reports, and handle tasks involving websites (Stephanie Palazzolo/The Information)
https://www.theinformation.com/articles/op

OpenAI Preps ChatGPT Agents in Challenge to Microsoft Excel and PowerPoint
As OpenAI tries to turn ChatGPT into a core application for white-collar work, it’s developing agent features that could make its customers less dependent on productivity apps from Microsoft. In the latest example, OpenAI has developed features that let ChatGPT customers quickly create and edit ...

Bin ja sehr froh, diesen tollen Erklärartikel zu Excel Power Query gefunden zu haben, selbstreferenzierende Tabellen tun genau das, was ich heute für eine Auswertung von Daten gesucht habe. Von selbst wäre ich da wahrscheinlich nie drauf gekommen, habe aber auch nicht die Zeit (und Lust), mich noch intensiver mit Excel zu befassen.
Henne oder Ei: Selbstreferenzierende Tabellen in Power Query | Der Tabellenexperte

[Thread] A new US paper shows the best frontier LLM models achieve 0% on hard real-life Programming Contest problems, domains where expert humans still excel (Rohan Paul/@rohanpaul_ai)
https://x.com/rohanpaul_ai/status/1934751145400111572

Rohan Paul (@rohanpaul_ai) on X
This is really BAD news of LLM's coding skill. ☹️ The best Frontier LLM models achieve 0% on hard real-life Programming Contest problems, domains where expert humans still excel. LiveCodeBench Pro, a benchmark composed of problems from Codeforces, ICPC, and IOI (“International

Einige der zuletzt hier besonders häufig geteilten #News:
Abschied bis Herbst: Dänisches Digitalministerium kehrt Microsoft den Rücken

Viele Fachverfahren in der Verwaltung laufen auf Excel – und viele Kommunen sind ohne Microsoft praktisch handlungsunfähig. Die Cloud-Strategie von Microsoft zwingt Behörden ab 2029 in die Abhängigkeit. Digitale Souveränität? Nur mit mutigen Schritten Richtung Open Source, wie sie Schleswig-Holstein jetzt wagt.
#FAZ Digitalwirtschaft: Wie digital souverän ist Deutschland – und ist das überhaup…

Cloud, KI, Cybersicherheit: Wie digital souverän ist Deutschland?
Cloud, KI, Cybersicherheit: Die neue Regierung möchte Deutschland robuster machen. Worum es geht.

🔧 #Generators excel at lazy iteration and memory efficiency, implementing Iterator interface for foreach loops
⚡ #Fibers enable cooperative multitasking and nested suspension, perfect for #CLI

Very cool, an Excel bug that keeps rolling back two weeks of edits even after I restore the last good version
Microsoft, keep up your generous marketing campaign for Google Workspace

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
Bo Wen, Guoyun Gao, Zhicheng Xu, Ruibin Mao, Xiaojuan Qi, X. Sharon Hu, Xunzhao Yin, Can Li
https://arxiv.org/abs/2507.12384

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models like Random Forest and XGBoost excel in interpretability and accuracy for tabular data, but scaling them remains computationally expensive due to poor data locality and high data dependence. Previous efforts to accelerate these models with analog content addressable memory (CAM) have struggled, due to the fact that the diff…

Detecting AI Assistance in Abstract Complex Tasks
Tyler King, Nikolos Gurney, John H. Miller, Volkan Ustun
https://arxiv.org/abs/2507.10761 https://…

Detecting AI Assistance in Abstract Complex Tasks
Detecting assistance from artificial intelligence is increasingly important as they become ubiquitous across complex tasks such as text generation, medical diagnosis, and autonomous driving. Aid detection is challenging for humans, especially when looking at abstract task data. Artificial neural networks excel at classification thanks to their ability to quickly learn from and process large amounts of data -- assuming appropriate preprocessing. We posit detecting help from AI as a classificatio…

Feature Complementation Architecture for Visual Place Recognition
Weiwei Wang, Meijia Wang, Haoyi Wang, Wenqiang Guo, Jiapan Guo, Changming Sun, Lingkun Ma, Weichuan Zhang
https://arxiv.org/abs/2506.12401

Feature Complementation Architecture for Visual Place Recognition
Visual place recognition (VPR) plays a crucial role in robotic localization and navigation. The key challenge lies in constructing feature representations that are robust to environmental changes. Existing methods typically adopt convolutional neural networks (CNNs) or vision Transformers (ViTs) as feature extractors. However, these architectures excel in different aspects -- CNNs are effective at capturing local details. At the same time, ViTs are better suited for modeling global context, mak…

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Xubo Qin, Jun Bai, Jiaqi Li, Zixia Jia, Zilong Zheng
https://arxiv.org/abs/2506.11603 https://

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or complex semantic understanding between queries and documents. One promising solution is to explicitly rewrite or augment queries using large language models (LLMs) to elicit reasoning-relevant content prior to retrieval. However, the widespread use of large-scale language models like GPT-4 or LLaMA3-70B remains impractical …

Von Word und Excel zu LibreOffice: Dänisches Ministerium verabschiedet Microsoft
Beim dänischen Digitalministerium sollen alle Angestellten ohne Microsoft auskommen. Stattdessen werde man Linux und LibreOffice nutzen, sagt die Ministerin.

CPN-Py: A Python-Based Tool for Modeling and Analyzing Colored Petri Nets
Alessandro Berti, Wil M. P. van der Aalst
https://arxiv.org/abs/2506.12238 https:…

CPN-Py: A Python-Based Tool for Modeling and Analyzing Colored Petri Nets
Colored Petri Nets (CPNs) are an established formalism for modeling processes where tokens carry data. Although tools like CPN Tools and CPN IDE excel at CPN-based simulation, they are often separate from modern data science ecosystems. Meanwhile, Python has become the de facto language for process mining, machine learning, and data analytics. In this paper, we introduce CPN-Py, a Python library that faithfully preserves the core concepts of Colored Petri Nets -- including color sets, timed tok…

ChatGPT Agent uses a virtual browser to do tasks such as filling online forms and calling public APIs, and generates downloadable PowerPoint and Excel files (Reece Rogers/Wired)
https://www.wired.com/story/openai-chatgpt-agent-launch/

OpenAI’s New ChatGPT Agent Tries to Do It All
It’s a PowerPoint generator! It’s a date-night planner! It’s … another agent from OpenAI.

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 110 nodes and 718 edges.
Tags: Biological, Food web, Unweighted, Metadata

LOL no.
https://masto.pt/@tugatech/115011897895923151

Dänemark (bzw. deren Digitalministerium) wollen ein paar Monate lang «Microsoft Detox» ausprobieren, also ohne Microsoft-Produkte auskommen.
https://www.heise.de/news/Von-Word-und-Excel-zu-LibreOffice-Daenisch…

Kann Deutschland nicht ein bisschen mehr wie Dänemark sein?
https://www.heise.de/news/Von-Word-und-Excel-zu-LibreOffice-Daenisches-Ministerium-verabschiedet-Microsoft-10438683.html

Abschied bis Herbst: Dänisches Digitalministerium kehrt Microsoft den Rücken
Beim dänischen Digitalministerium sollen alle Angestellten ohne Microsoft auskommen. Stattdessen werde man Linux und LibreOffice nutzen, sagt die Ministerin.

Generative Representational Learning of Foundation Models for Recommendation
Zheli Zhou, Chenxu Zhu, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
https://arxiv.org/abs/2506.11999

Generative Representational Learning of Foundation Models for Recommendation
Developing a single foundation model with the capability to excel across diverse tasks has been a long-standing objective in the field of artificial intelligence. As the wave of general-purpose foundation models sweeps across various domains, their influence has significantly extended to the field of recommendation systems. While recent efforts have explored recommendation foundation models for various generative tasks, they often overlook crucial embedding tasks and struggle with the complexit…

Einige der zuletzt hier besonders häufig geteilten #News:
Abschied bis Herbst: Dänisches Digitalministerium kehrt Microsoft den Rücken

Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction
Luyao Tang, Kunze Huang, Chaoqi Chen, Yuxuan Yuan, Chenxin Li, Xiaotong Tu, Xinghao Ding, Yue Huang
https://arxiv.org/abs/2508.10731

Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction
Human perceptual systems excel at inducing and recognizing objects across both known and novel categories, a capability far beyond current machine learning frameworks. While generalized category discovery (GCD) aims to bridge this gap, existing methods predominantly focus on optimizing objective functions. We present an orthogonal solution, inspired by the human cognitive process for novel object understanding: decomposing objects into visual primitives and establishing cross-knowledge comparis…

Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages
Imalsha Puranegedara, Themira Chathumina, Nisal Ranathunga, Nisansa de Silva, Surangika Ranathunga, Mokanarangan Thayaparan
https://arxiv.org/abs/2508.09091

Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages
Large Language Models (LLMs) excel in English, but their performance degrades significantly on low-resource languages (LRLs) due to English-centric training. While methods like LangBridge align LLMs with multilingual encoders such as the Massively Multilingual Text-to-Text Transfer Transformer (mT5), they typically use only the final encoder layer. We propose a novel architecture that fuses all intermediate layers, enriching the linguistic information passed to the LLM. Our approach features tw…

Training Camp Notebook 7/31: Michael Mayer, Brock Bowers continue to excel together https://www.raiders.com/news/training-camp-notebook-7-31-michael-mayer-brock-bowers-continue-to-excel-together

Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Technical Solutions
Parsa Omidi, Xingshuai Huang, Axel Laborieux, Bahareh Nikpour, Tianyu Shi, Armaghan Eshaghi
https://arxiv.org/abs/2508.10824

Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Technical Solutions
Memory is fundamental to intelligence, enabling learning, reasoning, and adaptability across biological and artificial systems. While Transformer architectures excel at sequence modeling, they face critical limitations in long-range context retention, continual learning, and knowledge integration. This review presents a unified framework bridging neuroscience principles, including dynamic multi-timescale memory, selective attention, and consolidation, with engineering advances in Memory-Augment…

Arbetsdagen lackar mot ett slut - nu ska jag logga ut och göra riktiga saker i den riktiga värld0en. Excel, Jamovi och PowerPoint får helt enkelt vänta till imorgon

✅ Das dänische Digitalministerium stellt schrittweise auf #Linux und #LibreOffice um.
Ziel ist es, die Abhängigkeit von #Microsoft und anderen US-Anbietern zu verringern. Die Umstellung soll b…

Abschied bis Herbst: Dänisches Digitalministerium kehrt Microsoft den Rücken
Beim dänischen Digitalministerium sollen alle Angestellten ohne Microsoft auskommen. Stattdessen werde man Linux und LibreOffice nutzen, sagt die Ministerin.

Einige der zuletzt hier besonders häufig geteilten #News:
Abschied bis Herbst: Dänisches Digitalministerium kehrt Microsoft den Rücken

La société qui gère la sécurité informatique de UBS travaille avec des fichiers Excel massifs. C'est pas une blague.
https://www.letemps.ch/cyber/plus-de-100-000-employes-d-ubs-touches-par-un-vol-massif-de-donnees-sensibles-affectant-aussi-pictet

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 105 nodes and 343 edges.
Tags: Biological, Food web, Unweighted, Metadata

liebes tagebuch, ich habe heute zum ersten mal mit pivot-tabellen in #excel herumgespielt. bislang bin ich gut ohne ausgekommen, aber dann ist das level jetzt auch freigespielt. 🙃

Attention-Based Map Encoding for Learning Generalized Legged Locomotion
Junzhe He, Chong Zhang, Fabian Jenelten, Ruben Grandia, Moritz B\"Acher, Marco Hutter
https://arxiv.org/abs/2506.09588

Attention-Based Map Encoding for Learning Generalized Legged Locomotion
Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. While traditional model-based controllers excel at planning on complex terrains, they struggle with real-world uncertainties. Learning-based controllers offer robustness to such uncertainties but often lack preci…

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 71 nodes and 155 edges.
Tags: Biological, Food web, Unweighted, Metadata

TLV-HGNN: Thinking Like a Vertex for Memory-efficient HGNN Inference
Dengke Han, Duo Wang, Mingyu Yan, Xiaochun Ye, Dongrui Fan
https://arxiv.org/abs/2508.07796 https://

TLV-HGNN: Thinking Like a Vertex for Memory-efficient HGNN Inference
Heterogeneous graph neural networks (HGNNs) excel at processing heterogeneous graph data and are widely applied in critical domains. In HGNN inference, the neighbor aggregation stage is the primary performance determinant, yet it suffers from two major sources of memory inefficiency. First, the commonly adopted per-semantic execution paradigm stores intermediate aggregation results for each semantic prior to semantic fusion, causing substantial memory expansion. Second, the aggregation process …

Mamba-FCS: Joint Spatio- Frequency Feature Fusion, Change-Guided Attention, and SeK Loss for Enhanced Semantic Change Detection in Remote Sensing
Buddhi Wijenayake, Athulya Ratnayake, Praveen Sumanasekara, Roshan Godaliyadda, Parakrama Ekanayake, Vijitha Herath, Nichula Wasalathilaka
https://arxiv.org/abs/2508.08232

Mamba-FCS: Joint Spatio- Frequency Feature Fusion, Change-Guided Attention, and SeK Loss for Enhanced Semantic Change Detection in Remote Sensing
Semantic Change Detection (SCD) from remote sensing imagery requires models balancing extensive spatial context, computational efficiency, and sensitivity to class-imbalanced land-cover transitions. While Convolutional Neural Networks excel at local feature extraction but lack global context, Transformers provide global modeling at high computational costs. Recent Mamba architectures based on state-space models offer compelling solutions through linear complexity and efficient long-range modeli…

Training Camp Notebook 7/31: Michael Mayer, Brock Bowers continue to excel together https://www.raiders.com/news/training-camp-notebook-7-31-michael-mayer-brock-bowers-continue-to-excel-together

This https://arxiv.org/abs/2312.17294 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their capabilities, existing approaches lack the flexibility to address diverse and ever-evolving user queries in open domains. Currently, there is also no existing dataset that evaluates LLMs on open-domain knowledge that requires tools to so…

Have you ever wanted to UNHIDE a specific row in Excel amidst a lot of hidden rows?
1. F5 & enter the cell of the row to unhide
2. Ctrl-Shift-( & it will unhide the row
(Ctrl-Shift-) & it will unhide the column)
▶️ Excel - How To Unhide A Specific Row - Episode 2561b
https://www.youtube.com/watch?v=wQU…

Progress, baby! #AI
Microsoft Redesigns Office Portal to Prioritize Copilot – Pixel Envy https://pxlnv.com/linklog/microsoft-copilot-portal/

Seriously, Apple? Wann kommt „Um den Finder weiter zu benutzen fehlen Dir noch Edelsteine. Kaufe hier in unserem Shop!“ 🙄

This https://arxiv.org/abs/2505.05335 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

FLAM: Frame-Wise Language-Audio Modeling
Recent multi-modal audio-language models (ALMs) excel at text-audio retrieval but struggle with frame-wise audio understanding. Prior works use temporal-aware labels or unsupervised training to improve frame-wise capabilities, but they still lack fine-grained labeling capability to pinpoint when an event occurs. While traditional sound event detection models can precisely localize events, they are limited to pre-defined categories, making them ineffective for real-world scenarios with out-of-di…

"En 2011, l’OMS accueille une nouvelle maladie dans son catalogue: la bigorexie. Initialement réservée aux adeptes des salles de musculation, le terme décrit désormais l’addiction au sport."
#santé #trail

Machine Learning Tools for the IceCube-Gen2 Optical Array
Francisco Javier Vara Carbonell (for the IceCube-Gen2 Collaboration), Jonas Selter (for the IceCube-Gen2 Collaboration)
https://arxiv.org/abs/2507.07844

Machine Learning Tools for the IceCube-Gen2 Optical Array
Neural networks (NNs) have a great potential for future neutrino telescopes such as IceCube-Gen2, the planned high-energy extension of the IceCube observatory. IceCube-Gen2 will feature new optical sensors with multiple photomultiplier tubes (PMTs) designed to provide omnidirectional sensitivity. Neural networks excel at handling high-dimensional problems and can naturally incorporate the increased complexity of these new sensors. Additionally, their fast inference time makes them promising can…

Memp: Exploring Agent Procedural Memory
Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
https://arxiv.org/abs/2508.06433

Memp: Exploring Agent Procedural Memory
Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or entangled in static parameters. In this work, we investigate strategies to endow agents with a learnable, updatable, and lifelong procedural memory. We propose Memp that distills past agent trajectories into both fine-grained, step-by-step instructions and higher-level, script-like abstractions, and explore the impact of different strategies for Build, …

Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
Maxime Fabre, Lyubov Dudchenko, Emre Neftci
https://arxiv.org/abs/2506.06374

Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
Multi-state spiking neurons such as the adaptive leaky integrate-and-fire (AdLIF) neuron offer compelling alternatives to conventional deep learning models thanks to their sparse binary activations, second-order nonlinear recurrent dynamics, and efficient hardware realizations. However, such internal dynamics can cause instabilities during inference and training, often limiting performance and scalability. Meanwhile, state space models (SSMs) excel in long sequence processing using linear state…

Masked Language Models are Good Heterogeneous Graph Generalizers
Jinyu Yang, Cheng Yang, Shanyuan Cui, Zeyuan Guo, Liangwei Yang, Muhan Zhang, Chuan Shi
https://arxiv.org/abs/2506.06157

Masked Language Models are Good Heterogeneous Graph Generalizers
Heterogeneous graph neural networks (HGNNs) excel at capturing structural and semantic information in heterogeneous graphs (HGs), while struggling to generalize across domains and tasks. Recently, some researchers have turned to integrating HGNNs with large language models (LLMs) for more generalizable heterogeneous graph learning. However, these approaches typically extract structural information via HGNNs as HG tokens, and disparities in embedding spaces between HGNNs and LLMs have been shown…

Learning Feasible Quantum States for Quadratic Constrained Binary Optimization Problems
Anthony Wilkie, Alexander DeLise, Andrew Del Real, Rebekah Herrman, James Ostrowski
https://arxiv.org/abs/2508.02590

Learning Feasible Quantum States for Quadratic Constrained Binary Optimization Problems
Quantum computing approaches excel at solving quadratic unconstrained binary optimization (QUBO) problems, however solving quadratic constrained binary optimization problems (QCBOs) is more challenging. In this work, we develop a variational approach that creates an equal superposition of quantum states that satisfy constraints in a QCBO. The method relies on flag qubits, one per constraint, to identify when a constraint is violated or not. The resulting equal superposition can be used as an in…

@… I'd suggest switching to Linux Mint or Zorin. LibreOffice has a spreadsheet program that is comparable to Excel.
There are lots of good articles on how to do this. For example:

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention
Yeonju Ro, Zhenyu Zhang, Souvik Kundu, Zhangyang Wang, Aditya Akella
https://arxiv.org/abs/2506.09316

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention
Large language models (LLMs) excel at capturing global token dependencies via self-attention but face prohibitive compute and memory costs on lengthy inputs. While sub-quadratic methods (e.g., linear attention) can reduce these costs, they often degrade accuracy due to overemphasizing recent tokens. In this work, we first propose \textit{dual-state linear attention} (\textbf{\dsla}), a novel design that maintains two specialized hidden states-one for preserving historical context and one for tr…

ODE-GS: Latent ODEs for Dynamic Scene Extrapolation with 3D Gaussian Splatting
Daniel Wang, Patrick Rim, Tian Tian, Alex Wong, Ganesh Sundaramoorthi
https://arxiv.org/abs/2506.05480

ODE-GS: Latent ODEs for Dynamic Scene Extrapolation with 3D Gaussian Splatting
We present ODE-GS, a novel method that unifies 3D Gaussian Splatting with latent neural ordinary differential equations (ODEs) to forecast dynamic 3D scenes far beyond the time span seen during training. Existing neural rendering systems - whether NeRF- or 3DGS-based - embed time directly in a deformation network and therefore excel at interpolation but collapse when asked to predict the future, where timestamps are strictly out-of-distribution. ODE-GS eliminates this dependency: after learning…

A bit baffled by my colleagues' and students' helplessness regarding "new" digital tools - considering that my grandpa, who is turning 97 in a week and is almost blind, is reading a lot of online news and tutorials ("TV is boring"), still sends e-cards for all our birthdays, recalculates all his care bills with a huge Excel table, and apparently has a LinkedIn account (he hasn't worked since 1990) 🤷‍♀️
(edit: typo)

Excel nervt so! Jetzt hatte ich endlich eine tolle Lösung mit Power Queries und einem Ordner mit mehreren öfter aktualisierten Tabellen als Datenquelle, da klappt das schon nicht mehr, wenn der Kram über OneDrive geteilt wird. Habe zwar eine Lösung für dynamische Pfade gefunden, aber die funktioniert leider nicht in Verbindung mit Onedrive, weil Excel dann die onedrive-Webadressen statt brav c:.... lokal zu liefern. Viel Zeit, keine Lösung. Aber jetzt erst mal nach Hause und heute Abend Cho…

you either die a hero or live long enough to see yourself become an excel power user

Industriespione erkennt man übrigens an der BahnCard 100 (incl. 1. Klasse).
Neben Excel-Tabellen und PPT-Präsentationen gibt's¹ Strategiepapiere und natürlich lautstarke Telefonate frei Haus.
__
¹https://toot.kif.rocks/@Hackhoernchen/114890037307189352 und …

Lets be honest, we spend too much time cleaning data. {janitor} can help with that: #rstats

This https://arxiv.org/abs/2506.04047 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

On Support Samples of Next Word Prediction
Language models excel in various tasks by making complex decisions, yet understanding the rationale behind these decisions remains a challenge. This paper investigates \emph{data-centric interpretability} in language models, focusing on the next-word prediction task. Using representer theorem, we identify two types of \emph{support samples}-those that either promote or deter specific predictions. Our findings reveal that being a support sample is an intrinsic property, predictable even before t…

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams
Wenxin Mao, Zhitao Wang Long Wang, Sirong Chen, Cuiyun Gao, Luyang Cao, Ziming Liu, Qiming Zhang, Jun Zhou, Zhi Jin
https://arxiv.org/abs/2508.03379

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams
Large language models (LLMs) excel at generating code from natural language (NL) descriptions. However, the plain textual descriptions are inherently ambiguous and often fail to capture complex requirements like intricate system behaviors, conditional logic, and architectural constraints; implicit data dependencies in service-oriented architectures are difficult to infer and handle correctly. To bridge this gap, we propose a novel step-by-step code generation framework named UML2Dep by leveragi…

Ugh. So twitter right now is WAY too effective at dividing the Dems. I hope we get this out of our system.
For the record, the big problem here is misplaced faith in the (white) left; e.g. Bernie's a good cheerleader but *wildly* ineffective on the ground because at the end of the day you have to work within the ugly system
a thing elder black folks in politics excel at, which the white left ALWAYS seems to forget

Min chef vill att jag ska lämna över ett jobb som jag skött till en kollega. Den kan inte mer än att öppna excel - jag jobbar med flera makron och pythonscript innan det går att lägga över filer i en databas. Kollegan kan inte heller programmet som statistiken lagras och behandlas i.
Sen ska saker tas ut och behandlas innan de kan användas för syftet de är till för.
Jag vet inte hur jag ska känna. En del av mig tycker att det är rimligt att chefen bestämmer vem som gör vad. Den a…

Shallow Deep Learning Can Still Excel in Fine-Grained Few-Shot Learning
Chaofei Qi, Chao Ye, Zhitai Liu, Weiyang Lin, Jianbin Qiu
https://arxiv.org/abs/2507.22041 https://

Shallow Deep Learning Can Still Excel in Fine-Grained Few-Shot Learning
Deep learning has witnessed the extensive utilization across a wide spectrum of domains, including fine-grained few-shot learning (FGFSL) which heavily depends on deep backbones. Nonetheless, shallower deep backbones such as ConvNet-4, are not commonly preferred because they're prone to extract a larger quantity of non-abstract visual attributes. In this paper, we initially re-evaluate the relationship between network depth and the ability to fully encode few-shot instances, and delve into whet…

Revisiting Gossip Protocols: A Vision for Emergent Coordination in Agentic Multi-Agent Systems
Mansura Habiba, Nafiul I. Khan
https://arxiv.org/abs/2508.01531 https://

Revisiting Gossip Protocols: A Vision for Emergent Coordination in Agentic Multi-Agent Systems
As agentic platforms scale, agents are evolving beyond static roles and fixed toolchains, creating a growing need for flexible, decentralized coordination. Today's structured communication protocols (e.g., direct agent-to-agent messaging) excel at reliability and task delegation, but they fall short in enabling emergent, swarm-like intelligence, where distributed agents continuously learn, adapt, and communicate to form collective cognition. This paper revisits gossip protocols, long valued in …

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Brian Liu, Rahul Mazumder, Peter Radchenko
https://arxiv.org/abs/2506.20114

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response. A key novelty of our estimator…

MTASet: A Tree-based Set for Efficient Range Queries in Update-heavy Workloads
Daniel Manor, Mor Perry, Moshe Sulamy
https://arxiv.org/abs/2507.20041 https://

MTASet: A Tree-based Set for Efficient Range Queries in Update-heavy Workloads
In concurrent data structures, the efficiency of set operations can vary significantly depending on the workload characteristics. Numerous concurrent set implementations are optimized and fine-tuned to excel in scenarios characterized by predominant read operations. However, they often perform poorly when confronted with workloads that heavily prioritize updates. Additionally, current leading-edge concurrent sets optimized for update-heavy tasks typically lack efficiency in handling atomic rang…

Loving Vampire lore and being a big fan of #BtVS Buffy The Vampire Slayer I went in to #Netflix The Discovery Of Witches as yeah, seen that, done that. But now into the 2nd season I'm really getting the vibe and story line. Both Theresa Palmer and Matthew Goode excel in their roles and bring …

This https://arxiv.org/abs/2505.19914 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Large Language Models (LLMs), such as OpenAI's o1 and DeepSeek's R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce Enigmata, the first comprehensive suite tailored for improving LLMs with puzzle reasoning skills. It includes 36 tasks across seven categories, each with 1) a generator that produces unlimited examples with controllable diffic…

Arbeitszeiterfassung: Mehrheit setzt BAG-Urteil um – trotz fehlender Regelungen
Deutsche Firmen setzen das BAG-Urteil zur Arbeitszeiterfassung mehrheitlich um, konkrete Regelungen fehlen aber. Apps, Excel und Stundenzettel sind im Einsatz.

ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion
Meng Zhou, Farzad Khalvati
https://arxiv.org/abs/2508.03008 https://

ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion
Multimodal medical image fusion integrates complementary information from different imaging modalities to enhance diagnostic accuracy and treatment planning. While deep learning methods have advanced performance, existing approaches face critical limitations: Convolutional Neural Networks (CNNs) excel at local feature extraction but struggle to model global context effectively, while Transformers achieve superior long-range modeling at the cost of quadratic computational complexity, limiting cl…

This https://arxiv.org/abs/2505.08421 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

e-GPU: An Open-Source and Configurable RISC-V Graphic Processing Unit for TinyAI Applications
Graphics processing units (GPUs) excel at parallel processing, but remain largely unexplored in ultra-low-power edge devices (TinyAI) due to their power and area limitations, as well as the lack of suitable programming frameworks. To address these challenges, this work introduces embedded GPU (e-GPU), an open-source and configurable RISC-V GPU platform designed for TinyAI devices. Its extensive configurability enables area and power optimization, while a dedicated Tiny-OpenCL implementation pro…

Compressing Chain-of-Thought in LLMs via Step Entropy
Zeju Li, Jianyuan Zhong, Ziyang Zheng, Xiangyu Wen, Zhijian Xu, Yingying Cheng, Fan Zhang, Qiang Xu
https://arxiv.org/abs/2508.03346

Compressing Chain-of-Thought in LLMs via Step Entropy
Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate verbose thought processes with considerable redundancy, leading to increased inference costs and reduced efficiency. We introduce a novel CoT compression framework based on step entropy, a metric that quantifies the informational contribution of individual reasoning steps to identify redundancy. Through theoretical analysis and extensive empirical validation on mathematical reasoning benc…

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
Chenhao Li, Marco Hutter, Andreas Krause
https://arxiv.org/abs/2507.05906 https://…

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations, with a focus on the structure of reward functions and their implications for policy learning. Feature-based methods offer dense, interpretable rewards that excel at high-fidelity motion imitation, yet often require sophisticated representations of references and struggle with generalization in unstructured settings. GAN-based methods, in contrast, use implicit, distributional s…

This https://arxiv.org/abs/2505.15304 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Saliency-Aware Quantized Imitation Learning for Efficient Robotic Control
Deep neural network (DNN)-based policy models, such as vision-language-action (VLA) models, excel at automating complex decision-making from multi-modal inputs. However, scaling these models greatly increases computational overhead, complicating deployment in resource-constrained settings like robot manipulation and autonomous driving. To address this, we propose Saliency-Aware Quantized Imitation Learning (SQIL), which combines quantization-aware training with a selective loss-weighting strate…

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 83 nodes and 335 edges.
Tags: Biological, Food web, Unweighted, Metadata

Attn Lawyers & Legal Assistants. Continuously updated guides for Legal Professionals are also available by subscription for Word, Excel, PowerPoint, Outlook & a special ed. for Word for Macintosh.

Original post: https://bsky.app/profile/did:plc:gwp6j

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Xunzhi Xiang, Yabo Chen, Guiyu Zhang, Zhongyu Wang, Zhe Gao, Quanming Xiang, Gonghu Shang, Junqi Liu, Haibin Huang, Yang Gao, Chi Zhang, Qi Fan, Xuelong Li
https://arxiv.org/abs/2508.03334

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Current autoregressive diffusion models excel at video generation but are generally limited to short temporal durations. Our theoretical analysis indicates that the autoregressive modeling typically suffers from temporal drift caused by error accumulation and hinders parallelization in long video synthesis. To address these limitations, we propose a novel planning-then-populating framework centered on Macro-from-Micro Planning (MMPL) for long video generation. MMPL sketches a global storyline f…

Hide and Seek with LLMs: An Adversarial Game for Sneaky Error Generation and Self-Improving Diagnosis
Rui Zou, Mengqi Wei, Yutao Zhu, Jirong Wen, Xin Zhao, Jing Chen
https://arxiv.org/abs/2508.03396

Hide and Seek with LLMs: An Adversarial Game for Sneaky Error Generation and Self-Improving Diagnosis
Large Language Models (LLMs) excel in reasoning and generation across domains, but still struggle with identifying and diagnosing complex errors. This stems mainly from training objectives that prioritize correct answers, limiting exposure to and learning from errors. While recent studies have begun to address this by introducing error signals, most rely on shallow, static errors, restricting improvement in deep diagnostic ability. To overcome this, we propose Hide and Seek Game (HSG), a dynami…

OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks
Zixuan Wang, Dingming Li, Hongxing Li, Shuo Chen, Yuchen Yan, Wenqi Zhang, Yongliang Shen, Weiming Lu, Jun Xiao, Yueting Zhuang
https://arxiv.org/abs/2508.05614

OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks
Large language models excel at abstract reasoning but their capacity for embodied agent reasoning remains largely unexplored. We present OmniEAR, a comprehensive framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks. Unlike existing benchmarks that provide predefined tool sets or explicit collaboration directives, OmniEAR requires agents to dynamically acquire capabilities and autonomously determine coordinati…

ACE-Step: A Step Towards Music Generation Foundation Model
Junmin Gong, Sean Zhao, Sen Wang, Shengyuan Xu, Joe Guo
https://arxiv.org/abs/2506.00045 https:/…

ACE-Step: A Step Towards Music Generation Foundation Model
We introduce ACE-Step, a novel open-source foundation model for music generation that overcomes key limitations of existing approaches and achieves state-of-the-art performance through a holistic architectural design. Current methods face inherent trade-offs between generation speed, musical coherence, and controllability. For example, LLM-based models (e.g. Yue, SongGen) excel at lyric alignment but suffer from slow inference and structural artifacts. Diffusion models (e.g. DiffRhythm), on the…

SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting
Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt
https://arxiv.org/abs/2508.03000

SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting
The growing demand for corporate sustainability transparency, particularly under new regulations like the EU Taxonomy, necessitates precise data extraction from large, unstructured corporate reports. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, requires high-quality, domain-specific question-answering (QA) datasets to excel at particular domains. To address this, we introduce SustainableQA, a novel dataset and a scalable pipeline for generating a comprehensive …

Scalable entangling gates on ion qubits via structured light addressing
Xueying Mai, Liyun Zhang, Qinyang Yu, Junhua Zhang, Yao Lu
https://arxiv.org/abs/2506.19535

Scalable entangling gates on ion qubits via structured light addressing
A central challenge in developing practical quantum processors is maintaining low control complexity while scaling to large numbers of qubits. Trapped-ion systems excel in small-scale operations and support rapid qubit scaling via long-chain architectures. However, their performance in larger systems is hindered by spectral crowding in radial motional modes, a problem that forces reliance on intricate pulse-shaping techniques to maintain gate fidelities. Here, we overcome this challenge by deve…

Image Generation from Contextually-Contradictory Prompts
Saar Huberman, Or Patashnik, Omer Dahary, Ron Mokady, Daniel Cohen-Or
https://arxiv.org/abs/2506.01929

Image Generation from Contextually-Contradictory Prompts
Text-to-image diffusion models excel at generating high-quality, diverse images from natural language prompts. However, they often fail to produce semantically accurate results when the prompt contains concept combinations that contradict their learned priors. We define this failure mode as contextual contradiction, where one concept implicitly negates another due to entangled associations learned during training. To address this, we propose a stage-aware prompt decomposition framework that gui…

Use Property-Based Testing to Bridge LLM Code Generation and Validation
Lehan He, Zeren Chen, Zhe Zhang, Jing Shao, Xiang Gao, Lu Sheng
https://arxiv.org/abs/2506.18315

Use Property-Based Testing to Bridge LLM Code Generation and Validation
Large Language Models (LLMs) excel at code generation, but ensuring their outputs to be functionally correct, especially in complex programming tasks, is a persistent challenge. While traditional Test-Driven Development (TDD) offers a path for code refinement, its efficacy with LLMs is often undermined by the scarcity of high-quality test cases or the pitfalls of automated test generation, including biased tests or inaccurate output predictions that can misdirect the correction process. This pa…

Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding
Tao Lin, Gen Li, Yilei Zhong, Yanwen Zou, Bo Zhao
https://arxiv.org/abs/2507.00416 …

Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding
Vision-Language-Action (VLA) models have emerged as a promising framework for enabling generalist robots capable of perceiving, reasoning, and acting in the real world. These models usually build upon pretrained Vision-Language Models (VLMs), which excel at semantic understanding due to large-scale text pretraining. However, VLMs typically lack precise spatial understanding capabilities, as they are primarily tuned on 2D image-text pairs without 3D supervision. To address this limitation, recen…

Xreal One Pro review: the impressive new $649 AR glasses resemble normal spectacles at a distance and excel as a high-def external display for PCs and phones (Chris Welch/Bloomberg)
https://www.bloomberg.com/news/features/20

VEDA: Efficient LLM Generation Through Voting-based KV Cache Eviction and Dataflow-flexible Accelerator
Zhican Wang, Hongxiang Fan, Haroon Waris, Gang Wang, Zhenyu Li, Jianfei Jiang, Yanan Sun, Guanghui He
https://arxiv.org/abs/2507.00797

VEDA: Efficient LLM Generation Through Voting-based KV Cache Eviction and Dataflow-flexible Accelerator
Large Language Models (LLMs) excel in natural language processing tasks but pose significant computational and memory challenges for edge deployment due to their intensive resource demands. This work addresses the efficiency of LLM inference by algorithm-hardware-dataflow tri-optimizations. We propose a novel voting-based KV cache eviction algorithm, balancing hardware efficiency and algorithm accuracy by adaptively identifying unimportant kv vectors. From a dataflow perspective, we introduce a…

This https://arxiv.org/abs/2505.07453 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

How well do LLMs reason over tabular data, really?
Large Language Models (LLMs) excel in natural language tasks, but less is known about their reasoning capabilities over tabular data. Prior analyses devise evaluation strategies that poorly reflect an LLM's realistic performance on tabular queries. Moreover, we have a limited understanding of the robustness of LLMs towards realistic variations in tabular inputs. Therefore, we ask: Can general-purpose LLMs reason over tabular data, really?, and focus on two questions 1) are tabular reasoning cap…

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 71 nodes and 148 edges.
Tags: Biological, Food web, Unweighted, Metadata

Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille
https://arxiv.org/abs/2506.22443

Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency. This work investigates a neuro-symbolic rule learning neural network named RL-Net that learns interpretable rule lists through neural optimization, applied for the first time to radar-based hand gesture recognition (HGR). We benchmark RL-Net against a fully transparent rule-based system (MIRA) and an explainable black-box model (XentricAI), evaluating a…

This https://arxiv.org/abs/2412.19302 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

RecLM: Recommendation Instruction Tuning
Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Networks (GNNs) excel at capturing user-item relationships, their effectiveness is limited when handling sparse data or zero-shot scenarios, primarily due to constraints in ID-based embedding functions. To address these challenges, we propose a model-agnostic recommendation instruction-tuning paradigm that seamlessly int…

MiCoTA: Bridging the Learnability Gap with Intermediate CoT and Teacher Assistants
Dongyi Ding, Tiannan Wang, Chenghao Zhu, Meiling Tao, Yuchen Eleanor Jiang, Wangchunshu Zhou
https://arxiv.org/abs/2507.01887

MiCoTA: Bridging the Learnability Gap with Intermediate CoT and Teacher Assistants
Large language models (LLMs) excel at reasoning tasks requiring long thought sequences for planning, reflection, and refinement. However, their substantial model size and high computational demands are impractical for widespread deployment. Yet, small language models (SLMs) often struggle to learn long-form CoT reasoning due to their limited capacity, a phenomenon we refer to as the "SLMs Learnability Gap". To address this, we introduce \textbf{Mi}d-\textbf{Co}T \textbf{T}eacher \textbf{A}ssist…

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation
Hayat Ullah, Syed Muhammad Talha Zaidi, Arslan Munir
https://arxiv.org/abs/2507.20996

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation
Convolutional neural networks (CNNs) excel in computer vision but are susceptible to adversarial attacks, crafted perturbations designed to mislead predictions. Despite advances in adversarial training, a gap persists between model accuracy and robustness. To mitigate this issue, in this paper, we present a multi-teacher adversarial robustness distillation using an adaptive learning strategy. Specifically, our proposed method first trained multiple clones of a baseline CNN model using an advers…

ACTLLM: Action Consistency Tuned Large Language Model
Jing Bi, Lianggong Bruce Wen, Zhang Liu, Chenliang Xu
https://arxiv.org/abs/2506.21250 https://

ACTLLM: Action Consistency Tuned Large Language Model
This paper introduces ACTLLM (Action Consistency Tuned Large Language Model), a novel approach for robot manipulation in dynamic environments. Traditional vision-based systems often struggle to learn visual representations that excel in both task execution and spatial reasoning, thereby limiting their adaptability in dynamic environments. ACTLLM addresses these challenges by harnessing language to craft structured scene descriptors, providing a uniform interface for both spatial understanding a…

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu
https://arxiv.org/abs/2506.01784 https://

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
While Large Language Models (LLMs) excel at many natural language processing tasks, they often suffer from factual inaccuracies in knowledge-intensive scenarios. Integrating external knowledge resources, particularly knowledge graphs (KGs), provides a transparent and updatable foundation for more reliable reasoning. Knowledge Base Question Answering (KBQA), which queries and reasons over KGs, is central to this effort, especially for complex, multi-hop queries. However, multi-hop reasoning pose…

Hierarchical Patch Compression for ColPali: Efficient Multi-Vector Document Retrieval with Dynamic Pruning and Quantization
Duong Bach
https://arxiv.org/abs/2506.21601

Hierarchical Patch Compression for ColPali: Efficient Multi-Vector Document Retrieval with Dynamic Pruning and Quantization
Multi-vector document retrieval systems, such as ColPali, excel in fine-grained matching for complex queries but incur significant storage and computational costs due to their reliance on high-dimensional patch embeddings and late-interaction scoring. To address these challenges, we propose HPC-ColPali, a Hierarchical Patch Compression framework that enhances the efficiency of ColPali while preserving its retrieval accuracy. Our approach integrates three innovative techniques: (1) K-Means quant…

Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies
Tao Xiong, Xavier Hu, Wenyan Fan, Shengyu Zhang
https://arxiv.org/abs/2507.00606

Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies
Large language models (LLMs) excel in complex tasks through advanced prompting techniques like Chain-of-Thought (CoT) and Tree-of-Thought (ToT), but their reliance on manually crafted, task-specific prompts limits adaptability and efficiency. We introduce Mixture of Reasoning (MoR), a training framework that embeds diverse reasoning strategies into LLMs for autonomous, task-adaptive reasoning without external prompt engineering. MoR has two phases: Thought Generation, creating reasoning chain t…

T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent
Hong Qing Yu
https://arxiv.org/abs/2506.18559 https://

T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent
Large language models excel at generating fluent text but frequently struggle with structured reasoning involving temporal constraints, causal relationships, and probabilistic reasoning. To address these limitations, we propose Temporal Causal Probabilistic Description Logic (T-CPDL), an integrated framework that extends traditional Description Logic with temporal interval operators, explicit causal relationships, and probabilistic annotations. We present two distinct variants of T-CPDL: one ca…

This https://arxiv.org/abs/2505.22649 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Pre-training for Recommendation Unlearning
Modern recommender systems powered by Graph Neural Networks (GNNs) excel at modeling complex user-item interactions, yet increasingly face scenarios requiring selective forgetting of training data. Beyond user requests to remove specific interactions due to privacy concerns or preference changes, regulatory frameworks mandate recommender systems' ability to eliminate the influence of certain user data from models. This recommendation unlearning challenge presents unique difficulties as removing…

DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router
Minghao Guo, Qingcheng Zeng, Xujiang Zhao, Yanchi Liu, Wenchao Yu, Mengnan Du, Haifeng Chen, Wei Cheng
https://arxiv.org/abs/2507.22050

DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router
Large Language Models (LLMs) excel at many reasoning tasks but struggle with knowledge-intensive queries due to their inability to dynamically access up-to-date or domain-specific information. Retrieval-Augmented Generation (RAG) has emerged as a promising solution, enabling LLMs to ground their responses in external sources. However, existing RAG methods lack fine-grained control over both the query and source sides, often resulting in noisy retrieval and shallow reasoning. In this work, we in…

Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models
Fangzhou Dong, Yifan Zeng, Yingpeng Sang, Hong Shen
https://arxiv.org/abs/2506.21360

Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models
Large Language Models (LLMs) excel in understanding and generating text but struggle with providing professional literary criticism for works with profound thoughts and complex narratives. This paper proposes GLASS (Greimas Literary Analysis via Semiotic Square), a structured analytical framework based on Greimas Semiotic Square (GSS), to enhance LLMs' ability to conduct in-depth literary analysis. GLASS facilitates the rapid dissection of narrative structures and deep meanings in narrative wor…

Memento: Note-Taking for Your Future Self
Chao Wan, Albert Gong, Mihir Mishra, Carl-Leander Henneking, Claas Beger, Kilian Q. Weinberger
https://arxiv.org/abs/2506.20642

Memento: Note-Taking for Your Future Self
Large language models (LLMs) excel at reasoning-only tasks, but struggle when reasoning must be tightly coupled with retrieval, as in multi-hop question answering. To overcome these limitations, we introduce a prompting strategy that first decomposes a complex question into smaller steps, then dynamically constructs a database of facts using LLMs, and finally pieces these facts together to solve the question. We show how this three-stage strategy, which we call Memento, can boost the performanc…

The Moral Gap of Large Language Models
Maciej Skorski, Alina Landowska
https://arxiv.org/abs/2507.18523 https://arxiv.org/pdf/2507.18523

The Moral Gap of Large Language Models
Moral foundation detection is crucial for analyzing social discourse and developing ethically-aligned AI systems. While large language models excel across diverse tasks, their performance on specialized moral reasoning remains unclear. This study provides the first comprehensive comparison between state-of-the-art LLMs and fine-tuned transformers across Twitter and Reddit datasets using ROC, PR, and DET curve analysis. Results reveal substantial performance gaps, with LLMs exhibiting high f…