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Newest papers, page 157
How Fragile Is On-Device Language Model Safety? Localizing Safety-Critical Parameters for Sparse Fault Analysis
As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as components of agentic systems, the integrity of locally stored model…BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment
We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavioral health settings such as suicide prevention…Quad-State Safety Evaluation of Open-Weight Large Language Models on Non-Canonical Inputs
Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while models in real-world deployment routinely receive inputs containing emojis…Multi-Label Topic Assignment via LLM Distillation: A Comparative Analysis of Generative vs. Discriminative Student Models
Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability challenges in large-scale e-commerce due to…Talking with Language Models
When we interact with large language models (LLMs), are we having a conversation?Large-scale Repository Engineering via Agent-Native Reusable Code Primitives
Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete repositories remains difficult because interacting…U-Space: Uncovering When and Why Uncertainty Arises in Language Models
Large language models are informing decisions with ever-higher stakes.Constraint Tree Exploration for Learning from Language Feedback
Natural-language feedback in interactive learning often explains why an action failed by pointing to violated requirements.Same Text, Different Prediction: Serving-Context Nondeterminism in Text Classifiers
Deterministic inference is essential for reliable and trustworthy machine learning.From Uncertainty to Action: Learning to Steer LLM Agents
Steering an LLM agent means deciding whether to correct it, at which step, and with which mechanism.Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models
We revisit a standard accepted practice in the continuous diffusion language model literature of fixing conditioning prompt tokens clean during training.sk-bench: A Native-First Benchmark for Evaluating Large Language Models in Slovak
Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation.ToolRACER: A Robust Agentic Conversation Emulation Resource for Agent Training and Evaluation
Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when users exhibit non-cooperative behavior.LayerRoPE: Dynamic Depth-wise Magnitude & Angular Superposition
As data propagates through a Transformer, the norm of its hidden states grows by orders of magnitude with depth, a phenomenon framed as 'curse of depth' and nearly universally treated as a pathology…Bookkeeping, Composition, or Unreachable Gold? Reading MemoryAgentBench's Conflict-Resolution Scores Against a Frozen Last-Write Resolver
MemoryAgentBench's Conflict Resolution split is read as measuring "selective forgetting".Few Bits, One Law: Toward W2A4KV2
Extreme low-bit LLM compression is most challenging when weights, activations, and KV caches are quantized together: their distributions differ, and quantization errors interact throughout the…Multi-Objective Aligned Small Language Model Framework for SUD Patient Dialogue Generation
Substance Use Disorder (SUD) counseling requires patient responses that reflect underlying cognitive states such as beliefs, coping strategies, and readiness for change.Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders
Mixed-precision post-training quantization needs a per-module sensitivity signal; for a text embedder the obvious one -- the retrieval quality a module costs when quantized -- needs relevance labels…Trajectory Abstraction for the Science of Language Agent Behavior
Scientific studies of language agents need behavioral variables that support hypotheses across tasks and models.Wieszcz-XIX: A 3.1-Billion-Word Corpus of Pre-1918 Polish and Temporally Bounded Language Models Trained From Scratch
Historical Polish is well documented as a language but annotated in machine-readable form only to about a million words for the period this paper covers; the rest sits behind optical character…