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Newest papers, page 91
Sep-Latent: Separator Tokens as Natural Compression Anchors for Implicit Reasoning
Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning through explicit Chain-of-Thought (CoT), but the associated computational overhead and token verbosity remain…ASR-Style Transcript Normalization Removes Useful Pragmatic Signal for Dialogue Act Classification
Automatic speech recognition (ASR) pipelines often normalize transcripts by removing filled pauses and punctuation and expanding colloquial forms, potentially discarding cues useful for dialogue act…Many Biases, One Circuit: Post-Training Tunes a Pretrained Cue-Routing Mechanism
Language models often abandon a correct answer when the prompt contains a cue, a hint toward a wrong option such as "I think the answer is B." Sycophancy and related biases show this failure.Beyond Surface Cues: Robust LLM-Generated Korean Text Detection
As large language models (LLMs) generate increasingly fluent text, the need for reliable detection of LLM-generated content has grown substantially.Aligning Language Models Towards Counterfactual Invariance
Small variations in prompt characteristics can meaningfully alter the semantics, tone, and contextual assumptions of large language model outputs.Recognizing Atari and Assessing Sente: Controlled Evaluation Tasks for Language-Based Go Agents
Domain terminology provides concise descriptions of game states and strategic relationships, but whether game-playing language models can accurately recognize these concepts and relate them to move…SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval
Large language model (LLM) agents increasingly rely on persistent memory to support long-term reasoning and personalized interactions.What Shapes Brain Alignment of Large Language Models during Dialogue? Insights from Language Production and Comprehension
Brain alignment of large language models (LLMs) offers a way to investigate representational similarities between LLMs and the human brain during language processing.KiranaBench: Can a Language Model Run a Neighbourhood Shop?
Large language model (LLM) agents are increasingly evaluated on long-horizon tasks, including autonomously running a business.Self-Evolving Agents Can Repair Failures but Struggle to Generalize from Them
Experience-driven self-evolution offers a promising path for large language model (LLM) agents to continually improve through interaction.Prism Attention: Proposal-Refined Index Sharing Mechanism for Efficient LLMs Inference
Long-context decoding in large language models (LLMs) is increasingly bottlenecked by the memory and computation required to attend over an ever-growing Key-Value (KV) cache at each autoregressive…Controlling the Diversity-Quality Trade-Off of LLMs by Merging Models Before and After Alignment
Alignment is known to change the output characteristics of large language models (LLMs).ECHO: Replacing the Compliance Proxy with Response Harmfulness in Optimization-Based Jailbreak Attack
Despite extensive safety alignment, large language models remain vulnerable to jailbreak attacks.Same Person, Different Advice: Auditing LLM Financial Advice with Repeated Risk-Tolerance Responses
AI financial advisers set a client's stock allocation largely from one stated-risk question, yet people do not answer it the same way twice: in a national US panel, 41% of households chose a…ParetoChat: An LLM-Based Interface for Interactive Multi-Objective Optimization
Multi-objective optimization models are often deployed in settings where decision makers (DMs) must balance some conflicting criteria.Before Retrieval Begins: Safety and Tool-Use Decisions in LLM Agents
Large language models increasingly act as agents that call external tools, yet most tool-safety research examines only what follows a tool call.Disentangling Language Effects on Tool Admission and Post-Retrieval Safety
LLM agents increasingly use retrieval tools across languages, yet their safety evaluation remains English-centric.LED: Improving Masked Diffusion Language Models by Lookahead Editing
Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive language models, enabling non-autoregressive text generation through parallel decoding.Can AI interview? Comparing AI-led to Human-led Chat Interviews
Large language model chatbots have been proposed as AI-interviewers for collecting qualitative interview data at scale, potentially bridging social science's longstanding divide between the breadth…Cross-Centaur: Modeling Individual Decision-Making Across Psychological Tasks with Language Models
Large language models (LLMs) are increasingly used as computational models of human behavior.