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Newest papers, page 107
Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation
Retrieving external knowledge can improve large language model (LLM) translation by providing supplementary information such as idiom interpretations and terminology.A Unified Response Framework for Questions and Tutoring Dialogues
Existing student models in educational applications largely focus on student responses to questions.Typed Decision Models on Tabular Data
Typed decision models are language models that answer a question with a fixed set of possible answers by returning a probability for each answer instead of generating text.GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation
Graph retrieval-augmented generation (GraphRAG) uses document-derived knowledge graphs to assemble evidence for large language model (LLM) answers.Evidence-Anchored Overdose Death Prediction from Death Investigation Reports
Confirmation of overdose deaths in the United States is often delayed due to the complexities of certifying drug-involved deaths.$τ$-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions
In long-horizon tool-calling tasks, LLM agents can fail not only because of tool-use errors, but also because of ineffective interaction with users.REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning
An early mistake in the generated reasoning can constrain all subsequent reasoning.INTACT: Inducing Latent Reasoning on a Frozen Backbone
Latent reasoning replaces natural-language chains of thought with compact continuous "soft-token" representations, reducing the number of intermediate tokens that must be decoded autoregressively.Who's Behind the Text? Robust Model Authorship Attribution
As large language models (LLMs) are increasingly used to generate text, their misuse has become a growing societal problem, with implications for academic misconduct, model distillation, and…Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs
Social psychology has long described how people can be persuaded to interpret a harmful act as moral.PA1-OPD: One-Step Prefix-Augmented On-Policy Distillation for Language Model Pretraining
Knowledge distillation (KD) improves language-model pretraining by matching teacher distributions at corpus prefixes, but provides no direct supervision at contexts induced by student predictions.PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference
Long-output inference in large language models suffers from severe KV cache memory and bandwidth bottlenecks.Twenty Days Later: A Pre-Registered Test of History-Based Forecasts of Brand Mentions in Gemini Answers
Counts of which brands a large language model names are useful only if they hold long enough to act on.Are We Really Measuring Forgetting? Pitfalls in Evaluating Machine Unlearning for Multimodal LLMs
As machine unlearning for multimodal large language models (MLLMs) rapidly advances, rigorous evaluation of its effectiveness becomes increasingly important.Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories
In this work, we reframe agentic trajectories as paths through a shared embedding space of actions and environmental observations.DAGFormer: Global Topology Prediction for Transformer Cross-Layer Connections
We propose DAGFormer, a Transformer language model whose cross-layer connections are planned by a jointly trained topology predictor.Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching
Large language models (LLMs) hold broad knowledge in one set of weights, but lack interpretability and trustworthy reasoning, especially in high-stakes use cases.J-tau: A Fully Localized Japanese Benchmark for Tool-Agent-User Interaction
We present J-tau, the first publicly available Japanese benchmark for evaluating tool-using LLM agents in interactive customer-support scenarios.Model What Matters: Decision-Guided Selective World Modeling for Language Agents
Reinforcement learning provides a direct mechanism for optimizing task objectives in large language model-driven agents, but task rewards are typically sparse trajectory-level signals that only…Selective Reasoning for Typed Decision Models: When to Start, Read, and Stop
Typed decision models need an interface that can use additional reasoning while retaining candidate outputs.