Tootfinder

Bayesian inference for the learning rate in Generalised Bayesian inference
Jeong Eun Lee, Sitong Liu, Geoff K. Nicholls
https://arxiv.org/abs/2506.12532 ht…

Bayesian inference for the learning rate in Generalised Bayesian inference
In Generalised Bayesian Inference (GBI), the learning rate and hyperparameters of the loss must be estimated. However, these inference-hyperparameters can't be estimated jointly with the other parameters by giving them a prior, as we discuss. Several methods for estimating the learning rate have been given which elicit and minimise a loss based on the goals of the overall inference (in our case, prediction of new data). However, in some settings there exists an unknown ``true'' learning rate ab…

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 …

Resa: Transparent Reasoning Models via SAEs
Shangshang Wang, Julian Asilis, \"Omer Faruk Akg\"ul, Enes Burak Bilgin, Ollie Liu, Deqing Fu, Willie Neiswanger
https://arxiv.org/abs/2506.09967

Resa: Transparent Reasoning Models via SAEs
How cost-effectively can we elicit strong reasoning in language models by leveraging their underlying representations? We answer this question with Resa, a family of 1.5B reasoning models trained via a novel and efficient sparse autoencoder tuning (SAE-Tuning) procedure. This method first trains an SAE to capture reasoning abilities from a source model, and then uses the trained SAE to guide a standard supervised fine-tuning process to elicit such abilities in a target model, all using verified…

Für die breite Verwendung von #KI, speziell im Kontext #Schule, muss sichergestellt sein, dass #LLMs user:innen nicht zu selbstgefährdendem Verhalten animieren.
Das Nonprofit Transluce arbeitet an verschie…

Surfacing Pathological Behaviors in Language Models
We train reinforcement learning (RL) agents to craft realistic natural-language prompts that elicit specified behaviors in frontier open-weight models (Llama 3.1/4, Qwen 2.5, and DeepSeek-V3), using a proposed variational lower bound to guide the search.

How to Elicit Explainability Requirements? A Comparison of Interviews, Focus Groups, and Surveys
Martin Obaidi, Jakob Droste, Hannah Deters, Marc Herrmann, Raymond Ochsner, Jil Kl\"under, Kurt Schneider
https://arxiv.org/abs/2505.23684

How to Elicit Explainability Requirements? A Comparison of Interviews, Focus Groups, and Surveys
As software systems grow increasingly complex, explainability has become a crucial non-functional requirement for transparency, user trust, and regulatory compliance. Eliciting explainability requirements is challenging, as different methods capture varying levels of detail and structure. This study examines the efficiency and effectiveness of three commonly used elicitation methods - focus groups, interviews, and online surveys - while also assessing the role of taxonomy usage in structuring a…

The Emotional Alignment Design Policy
Eric Schwitzgebel, Jeff Sebo
https://arxiv.org/abs/2507.06263 https://arxiv.org/pdf/2507.06263

The Emotional Alignment Design Policy
According to what we call the Emotional Alignment Design Policy, artificial entities should be designed to elicit emotional reactions from users that appropriately reflect the entities' capacities and moral status, or lack thereof. This principle can be violated in two ways: by designing an artificial system that elicits stronger or weaker emotional reactions than its capacities and moral status warrant (overshooting or undershooting), or by designing a system that elicits the wrong type of emo…

This https://arxiv.org/abs/2306.11154 has been replaced.
link: https://scholar.google.com/scholar?q=a

An Isotonic Mechanism for Overlapping Ownership
Motivated by the problem of improving peer review at large scientific conferences, this paper studies how to elicit self-evaluations to improve review scores in a natural many-to-many owner-item (e.g., author-paper) situation with overlapping ownership. We design a simple, efficient and truthful mechanism to elicit self-evaluations from item owners that can be used to calibrate their noisy review scores in the existing evaluation process (e.g., papers' review scores from peers). Our approach …

This https://arxiv.org/abs/2502.18504 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice
Jailbreaking large-language models (LLMs) involves testing their robustness against adversarial prompts and evaluating their ability to withstand prompt attacks that could elicit unauthorized or malicious responses. In this paper, we present TurboFuzzLLM, a mutation-based fuzzing technique for efficiently finding a collection of effective jailbreaking templates that, when combined with harmful questions, can lead a target LLM to produce harmful responses through black-box access via user prompt…

This https://arxiv.org/abs/2506.02878 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective
Chain-of-Thought (CoT) prompting has demonstrably enhanced the performance of Large Language Models on tasks requiring multi-step inference. This success has led to widespread claims of emergent reasoning capabilities in these models. In this paper, we present a theoretical counter-perspective: Chain-of-Thought (CoT) does not elicit genuine, abstract reasoning. Instead, we argue that Chain-of-Thought functions as a powerful structural constraint that guides Large Language Models to imitate the …

Inter(sectional) Alia(s): Ambiguity in Voice Agent Identity via Intersectional Japanese Self-Referents
Takao Fujii, Katie Seaborn, Madeleine Steeds, Jun Kato
https://arxiv.org/abs/2506.01998

Inter(sectional) Alia(s): Ambiguity in Voice Agent Identity via Intersectional Japanese Self-Referents
Conversational agents that mimic people have raised questions about the ethics of anthropomorphizing machines with human social identity cues. Critics have also questioned assumptions of identity neutrality in humanlike agents. Recent work has revealed that intersectional Japanese pronouns can elicit complex and sometimes evasive impressions of agent identity. Yet, the role of other "neutral" non-pronominal self-referents (NPSR) and voice as a socially expressive medium remains unexplored. In a…

Iterative Vickrey Auctions via Linear Programming
S\'ebastien Lahaie, Benjamin Lubin
https://arxiv.org/abs/2507.03252 https://arx…

Iterative Vickrey Auctions via Linear Programming
Building on the linear programming approach to competitive equilibrium pricing, we develop a general method for constructing iterative auctions that achieve Vickrey-Clarke-Groves (VCG) outcomes. We show how to transform a linear program characterizing competitive equilibrium prices into one that characterizes universal competitive equilibrium (UCE) prices, which elicit precisely the information needed to compute VCG payments. By applying a primal-dual algorithm to these transformed programs, we…

Legal Requirements Translation from Law
Anmol Singhal, Travis Breaux
https://arxiv.org/abs/2507.02846 https://arxiv.org/pdf/2507.0284…

Legal Requirements Translation from Law
Software systems must comply with legal regulations, which is a resource-intensive task, particularly for small organizations and startups lacking dedicated legal expertise. Extracting metadata from regulations to elicit legal requirements for software is a critical step to ensure compliance. However, it is a cumbersome task due to the length and complex nature of legal text. Although prior work has pursued automated methods for extracting structural and semantic metadata from legal text, key l…

AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
Aashray Reddy, Andrew Zagula, Nicholas Saban
https://arxiv.org/abs/2507.01020

AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
Large Language Models (LLMs) continue to exhibit vulnerabilities to jailbreaking attacks: carefully crafted malicious inputs intended to circumvent safety guardrails and elicit harmful responses. As such, we present AutoAdv, a novel framework that automates adversarial prompt generation to systematically evaluate and expose vulnerabilities in LLM safety mechanisms. Our approach leverages a parametric attacker LLM to produce semantically disguised malicious prompts through strategic rewriting te…

Social Robots for People with Dementia: A Literature Review on Deception from Design to Perception
Fan Wang, Giulia Perugia, Yuan Feng, Wijnand IJsselsteijn
https://arxiv.org/abs/2507.00963

Social Robots for People with Dementia: A Literature Review on Deception from Design to Perception
As social robots increasingly enter dementia care, concerns about deception, intentional or not, are gaining attention. Yet, how robotic design cues might elicit misleading perceptions in people with dementia, and how these perceptions arise, remains insufficiently understood. In this scoping review, we examined 26 empirical studies on interactions between people with dementia and physical social robots. We identify four key design cue categories that may influence deceptive impressions: cues r…

This https://arxiv.org/abs/2307.15239 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Distinctive pupil and microsaccade-rate signatures in self-recognition
Pupil dynamics and fixational eye movements are primarily involuntary processes that actively support visual perception during fixations. Both measures are known to be sensitive to ongoing cognitive and affective processing. In a visual fixation experiment (N=116) we demonstrate that self-recognition, familiar faces, and unfamiliar faces elicit specific responses in pupil dynamics and microsaccade rate. First, the pupil response comprises an immediate pupil constriction followed by a dilation i…

This https://arxiv.org/abs/2412.03554 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Categorize and randomize: a permissive model of stochastic choice
We model stochastic choices with categorization. The agent preliminarly groups alternatives in homogenous disjoint classes, then randomly chooses one class and randomly picks an item within the selected class. We give a formal definition of a choice generated by this procedure, and provide an axiomatic characterization. The characterizing properties allow an external analyst to elicit that categorization is applied. In a broader interpretation, the model allows to describe the observed choice a…

Linearly Decoding Refused Knowledge in Aligned Language Models
Aryan Shrivastava, Ari Holtzman
https://arxiv.org/abs/2507.00239 https://

Linearly Decoding Refused Knowledge in Aligned Language Models
Most commonly used language models (LMs) are instruction-tuned and aligned using a combination of fine-tuning and reinforcement learning, causing them to refuse users requests deemed harmful by the model. However, jailbreak prompts can often bypass these refusal mechanisms and elicit harmful responses. In this work, we study the extent to which information accessed via jailbreak prompts is decodable using linear probes trained on LM hidden states. We show that a great deal of initially refused …

Stochastically Dominant Peer Prediction
Yichi Zhang, Shengwei Xu, David Pennock, Grant Schoenebeck
https://arxiv.org/abs/2506.02259 https://

Stochastically Dominant Peer Prediction
Eliciting reliable human feedback is essential for many machine learning tasks, such as learning from noisy labels and aligning AI systems with human preferences. Peer prediction mechanisms incentivize truthful reporting without ground truth verification by scoring agents based on correlations with peers. Traditional mechanisms, which ensure that truth-telling maximizes the expected scores in equilibrium, can elicit honest information while assuming agents' utilities are linear functions of the…

Security Assessment of DeepSeek and GPT Series Models against Jailbreak Attacks
Xiaodong Wu, Xiangman Li, Jianbing Ni
https://arxiv.org/abs/2506.18543 http…

Security Assessment of DeepSeek and GPT Series Models against Jailbreak Attacks
The widespread deployment of large language models (LLMs) has raised critical concerns over their vulnerability to jailbreak attacks, i.e., adversarial prompts that bypass alignment mechanisms and elicit harmful or policy-violating outputs. While proprietary models like GPT-4 have undergone extensive evaluation, the robustness of emerging open-source alternatives such as DeepSeek remains largely underexplored, despite their growing adoption in real-world applications. In this paper, we present …

Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
Ruiwei Xiao, Xinying Hou, Runlong Ye, Majeed Kazemitabaar, Nicholas Diana, Michael Liut, John Stamper
https://arxiv.org/abs/2506.19107

Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
With the proliferation of large language model (LLM) applications since 2022, their use in education has sparked both excitement and concern. Recent studies consistently highlight students' (mis)use of LLMs can hinder learning outcomes. This work aims to teach students how to effectively prompt LLMs to improve their learning. We first proposed pedagogical prompting, a theoretically-grounded new concept to elicit learning-oriented responses from LLMs. To move from concept design to a proof-of-co…

Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior
Chengyuan Zhang, Cathy Wu, Lijun Sun
https://arxiv.org/abs/2506.14762

Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior
Accurate and interpretable car-following models are essential for traffic simulation and autonomous vehicle development. However, classical models like the Intelligent Driver Model (IDM) are fundamentally limited by their parsimonious and single-regime structure. They fail to capture the multi-modal nature of human driving, where a single driving state (e.g., speed, relative speed, and gap) can elicit many different driver actions. This forces the model to average across distinct behaviors, red…