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Newest papers, page 115
Encoding Context into LLM Weights: What Makes It Context?
Longer contexts allow large language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with context length.Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively
The sycophancy of language models, or their failure to push back against faulty user input, is now widely studied.SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models
Agent skills turn natural-language instructions into actions, creating a security challenge: legitimate automation and malicious behaviour can look deceptively similar.Understanding Machine Taste: Aesthetic Agreement Across Foundation Models in K-pop
As foundation models enter creative industries, understanding their aesthetic preferences becomes an important step toward AI-assisted creative decision making.Geometric Structure Predicts Per-Example Neighborhood Loss Under PCA Truncation
Dimensionality reduction can preserve the average performance of text embeddings even when a substantial fraction of their dimensions is removed.Auditing Failure Attribution: Label Predictability and Judge Behavior
Failure-attribution benchmarks ask an LLM judge which step or agent caused a failure.Connecting the Dots: Enduring Performance Gaps in ASR Systems Between Standard and Minority English Varieties
Automatic Speech Recognition (ASR) system evaluations have consistently revealed performance disparities along lines of race, gender, socioeconomic status, and language variety.Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking
Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overthinking challenge that allocates uniformly heavy…More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026
A model trained on readers' highlights can show a newcomer where earlier readers converged on a page, even on pages with few readers.Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders
A recommendation request on Reddit draws several replies, each naming different titles.Beyond Belief Nesting: Rethinking Higher-Order Theory of Mind in Large Language Models
Theory of mind (ToM), the ability to reason about others' mental states, underlies effective communication and coordination.GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria
Research on gender-related online content is often operationalized through harm-centered tasks that collapse non-harmful gender-related and unrelated content, while existing datasets remain…Full-duplex Spoken Language Modeling with Discrete Diffusion
We introduce Diffuplex, a full-duplex spoken language model based on discrete diffusion that achieves an unprecedented balance between linguistic intelligence and response efficiency.Where Words Begin and End
Forced aligners locate each word of a known transcript in the audio, and many speech pipelines depend on them.BRIDGE: Selective Boundary Repair with Dependency Graph Evidence in Multi-Agent Systems
Multi-agent collaboration has emerged as a critical paradigm for tackling complex, long-horizon tasks; however, while coordination protocols continue to advance, systemic failures at the inter-agent…Perturbation, Not Provenance: Why Activation Steering Does Not Make a Robust Text Watermark
Linear directions in the activation space of large language models causally and universally influence model behavior.Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR
Reinforcement learning with verifiable rewards (RLVR) has become a principal recipe for improving the reasoning of large language models (LLMs).Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing
Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis rather than edit prediction.Prefilling-dLLM v2: Learned Chunk Routing for Long-Context Diffusion Language Models
Diffusion language models apply bidirectional attention over the whole context at every denoising step, so a longer context makes each step substantially more expensive.Cross-Lingual Transfer of Evil Vectors Is Directionally Asymmetric
Persona vectors are linear directions in the residual-stream activations of LLMs that encode behavioral traits and enable fine-grained control over model behavior.