Tootfinder

How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations
Yoshiki Takenami, Yin Jou Huang, Yugo Murawaki, Chenhui Chu
https://arxiv.org/abs/2508.21137

How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations
Cognitive biases, well-studied in humans, can also be observed in LLMs, affecting their reliability in real-world applications. This paper investigates the anchoring effect in LLM-driven price negotiations. To this end, we instructed seller LLM agents to apply the anchoring effect and evaluated negotiations using not only an objective metric but also a subjective metric. Experimental results show that LLMs are influenced by the anchoring effect like humans. Additionally, we investigated the rel…

Trump is very, very angry about a New York Times piece
that carefully documents his obvious physical decline.
Trump raged over the piece,
letting out a stream of lies about his performance in office
and even attacking the reporter who wrote the piece as ugly.
This may seem typical of Trump,
but we think it gets at something deeper.
Trump’s entire political mystique is premised on the fiction that he’s a strong, virile, formidable figure
who …

Transcript: Trump Fury Erupts at NYT as Mental Decline Visibly Worsens
As Trump’s rage at the paper of record reveals too much, the author of piece on Trump as “lame duck dictator” explains how devastating it is for him that his carefully-cultivated illusion of strength is collapsing.

Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs?
Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
https://arxiv.org/abs/2509.25107

Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs?
The advent of Large Language Models (LLMs) has significantly advanced web-based Question Answering (QA) systems over semi-structured content, raising questions about the continued utility of knowledge extraction for question answering. This paper investigates the value of triple extraction in this new paradigm by extending an existing benchmark with knowledge extraction annotations and evaluating commercial and open-source LLMs of varying sizes. Our results show that web-scale knowledge extract…

RIS- and Multi-Snapshot-Enabled SISO 3D Position and Velocity Estimation With Single Base Station
Yirun Wang, Yongqing Wang, Yuyao Shen, Gongpu Wang, Chintha Tellambura
https://arxiv.org/abs/2509.23274

RIS- and Multi-Snapshot-Enabled SISO 3D Position and Velocity Estimation With Single Base Station
Reconfigurable intelligent surface (RIS) panels can act as cost-effective anchors for radio localization, complementing conventional base station (BS) anchors. This paper investigates joint three-dimensional position and velocity estimation (3D-JPVE) in single-input single-output (SISO) systems with only one BS available. We first theoretically show that 3D-JPVE is infeasible when relying solely on a single RIS or on multiple snapshots alone. To address this, we propose combining RIS deployment…

Scale-GS: Efficient Scalable Gaussian Splatting via Redundancy-filtering Training on Streaming Content
Jiayu Yang, Weijian Su, Songqian Zhang, Yuqi Han, Jinli Suo, Qiang Zhang
https://arxiv.org/abs/2508.21444

Scale-GS: Efficient Scalable Gaussian Splatting via Redundancy-filtering Training on Streaming Content
3D Gaussian Splatting (3DGS) enables high-fidelity real-time rendering, a key requirement for immersive applications. However, the extension of 3DGS to dynamic scenes remains limitations on the substantial data volume of dense Gaussians and the prolonged training time required for each frame. This paper presents \M, a scalable Gaussian Splatting framework designed for efficient training in streaming tasks. Specifically, Gaussian spheres are hierarchically organized by scale within an anchor-bas…

Diffeomorphism Invariance and Background Independence
Kurniawan Tjandra, Kuldip Singh
https://arxiv.org/abs/2509.26450 https://arxiv.org/pdf/2509.26450

Diffeomorphism Invariance and Background Independence
This paper answers examines the relationship between Diffeomorphism Invariance and Background Independence. First, a review of the relationship between Background Independence, General Relativity (GR) and pre-GR theories are given. Then, a wide range of other conceptions of background independence is discussed. It is shown that the definition of Background Independence is fluid and can mean different things to different philosophers and/or physicists. Most pertinently, the paper addresses the q…

Local and global Langlands conjecture(s) over function fields
Dennis Gaitsgory
https://arxiv.org/abs/2509.24902 https://arxiv.org/pdf/2509.24902

Local and global Langlands conjecture(s) over function fields
This is a write-up for the plenary ICM talk, 2026. The goal of this paper is to propose a set of conjectures whose aim is to answer the basic question of the Langlands program (over function fields): how to describe the space of automorphic functions in terms of the spectral side (i.e., Langlands parameters)?

Evaluating Open-Source Large Language Models for Technical Telecom Question Answering
Arina Caraus, Alessio Buscemi, Sumit Kumar, Ion Turcanu
https://arxiv.org/abs/2509.21949 ht…

Evaluating Open-Source Large Language Models for Technical Telecom Question Answering
Large Language Models (LLMs) have shown remarkable capabilities across various fields. However, their performance in technical domains such as telecommunications remains underexplored. This paper evaluates two open-source LLMs, Gemma 3 27B and DeepSeek R1 32B, on factual and reasoning-based questions derived from advanced wireless communications material. We construct a benchmark of 105 question-answer pairs and assess performance using lexical metrics, semantic similarity, and LLM-as-a-judge s…

JGU Mainz's Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA
Hossain Shaikh Saadi, Minh Duc Bui, Mario Sanz-Guerrero, Katharina von der Wense
https://arxiv.org/abs/2509.22490

JGU Mainz's Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA
This paper presents the JGU Mainz submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: Machine Translation and Question Answering, focusing on Ukrainian, Upper Sorbian, and Lower Sorbian. For each language, we jointly fine-tune a Qwen2.5-3B-Instruct model for both tasks with parameter-efficient finetuning. Our pipeline integrates additional translation and multiple-choice question answering (QA) data. For Ukrainian QA, we further use retrieval-augmented gener…

Single Answer is Not Enough: On Generating Ranked Lists with Medical Reasoning Models
Pittawat Taveekitworachai, Natpatchara Pongjirapat, Krittaphas Chaisutyakorn, Piyalitt Ittichaiwong, Tossaporn Saengja, Kunat Pipatanakul
https://arxiv.org/abs/2509.20866

Single Answer is Not Enough: On Generating Ranked Lists with Medical Reasoning Models
This paper presents a systematic study on enabling medical reasoning models (MRMs) to generate ranked lists of answers for open-ended questions. Clinical decision-making rarely relies on a single answer but instead considers multiple options, reducing the risks of narrow perspectives. Yet current MRMs are typically trained to produce only one answer, even in open-ended settings. We propose an alternative format: ranked lists and investigate two approaches: prompting and fine-tuning. While promp…