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Newest papers, page 64
Intergroup Bias Predicts Expressions of Social Judgment and Empathy in the r/AmITheAsshole Reddit Community
People share and understand others' experiences differently based on their social identities; often, people empathize more when the other person belongs to their ingroup compared to an outgroup.Hierarchical Masked Diffusion Language Models
Masked diffusion language models have emerged as an alternative to autoregressive models, enabling parallel token generation.Heard But Not Said: Prompting Understates Temporal Information of Musical Moments in Frozen Large Audio-Language Models
Large audio-language models (LALMs) understand music, yet struggle to locate a described moment in time.Computer Vision and Knowledge-Grounded Language Models for Intelligent Beekeeper Assistance
Managed and wild bees sustain roughly a third of the global food products, yet colonies face mounting stress from climate-change including extreme weather patterns, altered parasite and disease…Credit Where Credit Is Due: Contrastive Attribution Topology Search for Multi-Agent Systems
An LLM multi-agent system (MAS) is only as good as the agents it chooses to trust: its accuracy and cost hinge on its topology, meaning which agents are included and how they communicate.Same Probabilities, Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision Support
Large language models (LLMs) are increasingly used to support consequential decisions, yet their reliability under equivalent representations of uncertainty remains unclear.ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery
Evolutionary coding agents such as AlphaEvolve are one of the notable successes in using Large Language Models (LLMs) to build AI Scientists.LLM-Based Exogenous World Modeling for Dynamic Scheduling
Dynamic scheduling requires dispatching online arrivals while anticipating future jobs, as poor decisions can compound downstream costs under nonlinear tardiness objectives.Asking What a Director Sees Can Change What Vision-Language Models Choose
Vision-language models are now routinely tested on theory of mind: whether they can work out what another person sees, knows, and means.OSynth: A Longitudinal, Context-Sensitive Evaluation of Meeting Understanding
Meeting notetakers are increasingly deployed in settings where longitudinal context is available including calendar metadata, prior meetings, emails, slides, and speaker enrollments; however…Signals and Queries: Token-Efficient Agentic Context Discovery
User-facing LLM agents must identify which behavioral policies apply at each turn, but evaluating every policy with an LLM is costly.OACap: Caption-Guided Active Observation for Detailed Video Captioning
Unlike question answering, where a query specifies a perceptual target, open-ended detailed captioning must determine what evidence to acquire and how to integrate it into a coherent global…Seeing the Forest for the Trees: Emergent Agreement from Individual Conversations
As LLM agents interact in the wild and shape each other's decisions, understanding how they converge toward agreement becomes important.Policy Central: Detecting Conflicts, Redundancy, and Similarity in Deployed Police Policy
Policy Central is a deployed system that helps police departments find relationships between sections of their written policy: whether two sections conflict, are redundant, are merely similar, or…ChronoSynth: Constraint-Aware Question Synthesis over Temporal Knowledge Graphs
Knowledge graph question generation (KGQG) generates natural-language questions from structured graph evidence and a target answer.Reader--Writer Coupling in Transformer MLPs
A transformer MLP gives each hidden channel two roles: a reader that decides when the channel responds, and a writer that decides what it adds back to the residual stream.Uncensored Is Not Unbiased: Abliteration Removes Refusals but Not Script-Dependent Bias About Taiwan
"Abliterated" ("uncensored") builds of open-weight language models remove the activation direction that mediates refusal, and are popular partly on the assumption that a model that no longer refuses…Decodability Is Not Controllability: Local and Anisotropic Speaker Control in Continuous-Latent TTS
High linear decodability does not entail causal controllability in continuous-latent text-to-speech (TTS).Data Schedules and Learning Rate Schedules Should Be Designed Together
Mid-training (MT) is a training phase in LLM training pipelines that continues pretraining (PT) on smaller datasets of higher value.B-CARE: Blind Classwise Assessment of Relative Evidence for Stance Error Ranking
Stance classifiers make errors that can be costly to identify under limited human-review budgets.