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Newest papers, page 41
Metacognitive Decoding: Efficient Inference for Diffusion Language Agents
Diffusion language models offer a promising path to efficient agents through parallel token generation.Multi-State Linear Attention with Generalized Second-Order Routing
Self-attention plays a key role in long-sequence modeling, but its quadratic time complexity fundamentally limits the scaling of Large Language Models.Can LLMs Uncover Hidden Physical Laws through Free Exploration?
Scientific discovery often begins with an unexpected observation that prompts a new research question.GEPA-Curator: Curated Learning for Self Improving Agents
Large Language Models (LLMs) are increasingly used in multi-turn agentic workloads that require sustained interaction with complex environments.Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness
We introduce Heads of Security (HoS), a prompt injection defense that enforces instruction-data separation via a small set of learned per-head attention gates.What a Weight Update Buys: Leverage Across Training Stages
Post-training moves a language model's weights by about a thousandth of their norm and changes its behavior sharply; pretraining moves them by a third and buys a slow drift.ThinkJev: One Thought, Every Budget for Calibrated Typed Decisions
Many uses of language models end in a typed decision: a choice among a closed set of options, whose probabilities code can act on.OpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery
Large language models (LLMs) have shown increasing promise in research-level problem solving, yet a comprehensive collection of such problems is still missing, and evaluating them on research-level…Agentic Backdoors via Pretraining Poisoning
Large language models are pretrained on vast, uncurated corpora scraped from the public web.Multi-Draft On-Policy Distillation for Diffusion Drafters
Speculative decoding is used to reduce the inference cost of language models.Locating Sycophancy Across Training Stages in Open Language Models
Language model assistants sometimes change a correct answer when the user suggests a wrong one.DeFT-RAG: Decomposing Multi-Hop Questions into Self-Contained Sub-Questions with Reusable Answers
Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant passages from a large corpus.Detecting Covert Collusion between AI Agents through Partner-Outsider Positive Listening
AI agents that share a secret convention can coordinate through messages that look innocent, so a monitor that reads only the messages can miss them.Human-Residual Steering: Aligning Frozen LLM Judges with Human Judgment Distributions
Large language models (LLMs) are widely used as automated judges, yet most alignment methods target aggregated human judgments and overlook variation among individual annotators.Think Twice: The Metis-1.6 Technical Report
Metis is a bet that better data, better training methods and new architectures can buy what a bigger parameter count cannot, at a fraction of the cost.Beyond Utterance-Level Speaker Similarity: Perception-Aligned Segmental Evaluation of Accent in Speech Generation
Utterance-level similarity ratings do not reveal where perceived accent differences occur in generated speech.Grounding Long Financial Text with Semantically-Enriched Tables via Multimodal Learning
We propose a multimodal transformer framework for efficiently modeling long1 financial documents that combine structured tabular data with noisy, unstructured2 textual disclosures.Muon Makes the First Mixture-of-Experts Layer Harder to Balance
Many open mixture-of-experts (MoE) language models train with Muon instead of AdamW.SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World
Large language model agents are increasingly used for scientific discovery.FAD-KV: Adaptive Dictionary KV Caching for Efficient Reasoning Decoding
Key-value (KV) caching is a core mechanism in large language models, where intermediate key and value representations are stored to accelerate autoregressive decoding.