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Newest papers, page 193
How Much Is an AI Token Worth? Scaling Laws for Wild AI-Generated Web Text
Web text makes up the majority of pretraining data and is increasingly AI-generated.Scaling Laws for Looped Mixture of Experts
Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed parameters, while MoE sparsity expands total…MatLoom: Layered Text-to-Material Generation in a Compact Program Space
Material generation should produce not only an appearance, but also the rules that construct it.EvoDuet: Bilevel Co-Evolution of Web Searching and Task Solving for Scientific Discovery
Evolutionary search with large language models (LLMs) can stall when progress requires external knowledge the model lacks.Semifactual Credit-Augmented Policy Optimization
Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet their predictions remain sensitive to task-irrelevant prompt…Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis
Multimodal large language models (MLLMs) are rapidly advancing clinical diagnosis, yet their adaptation pipelines remain anchored to accuracy-based objectives.A Shared Taste for Model-Written Text: The Generator-by-Selector Matrices of "AI-AI Bias" Show No Detectable Own-Model Premium
Laurito et al. (PNAS 2025) showed that large language models choosing between two descriptions of the same product, paper or film prefer the description written by a language model over the one…When Do Attention-Head Ablations Support Causal Claims? Projection-Level Confounds, Floor Effects, and Matched Controls
Attention-head ablation, zeroing a head and measuring the resulting change in task performance, is a common method for inferring which components of a language model are causally responsible for a…Faithful Chart Generation for Multimodal Deep Research: Frame-Evidence Co-Adaptation
Analytical charts in multimodal deep research encode quantitative claims, requiring every visualized value to be faithfully grounded in supporting evidence.FAER: Auditable Utility-Aligned Trajectory Replay for Language Model Post-Training
Replay selectors often rank cached trajectories by format feedback, confidence, freshness, or response length, although cache-level correctness and downstream learner utility are distinct objectives.Dissonant ballerinas and crafty carrots: a comparative multi-modal analysis of Italian brain rot
This paper presents a comparative multi-modal analysis of Italian and Romanian brain rot memes, investigating the factors that contribute to its appeal and the linguistic and cultural distinctions…LLM-as-a-Judge for Low-Resource Languages: Adapting Ragas and Comparative Ranking for Romanian
Evaluating Retrieval-Augmented Generation (RAG) systems remains a challenge for Low-Resource Languages (LRLs), where standard reference-based metrics fall short.UniBuc at SemEval-2024 Task 2: Tailored Prompting with Solar for Clinical NLI
This paper describes the approach of the UniBuc team in tackling the SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials.Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluation
Can a text-to-3D leaderboard change when every generated scene stays fixed?EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights
When Isaac Newton discovered the law of gravitation, he did so through an iterative process of analyzing observed data such as planetary patterns, finding the underlying mechanisms by describing…Rules Amortize, Pairings Don't: Linguistic Structure Determines What Latent Task Representations Can Replace In-Context Learning
In-context learning (ICL) can be amortized into latent objects (task vectors, function vectors, context vectors) that recover few-shot behavior at zero-shot inference cost, but recent theory shows a…Assessing the Impact of Language Disparity on Multilingual Linguistic Ability in Large Language Models
Claims about the grammatical competence of multilingual language models vary sharply with how competence is measured, yet the interaction between evaluation paradigm, post-training, and language…Can LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning
Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient histories.Emergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness
Large language models are characterized by three key properties: capability, alignment, and faithfulness.Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL
Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously.