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Newest papers, page 78
Large-Scale Biomedical RAG: Corpus Scaling, Document Representation, and Efficiency Trade-offs
We present a controlled, large-scale evaluation of how corpus scale, document representation, and reranking affect the effectiveness and efficiency of biomedical Retrieval-Augmented Generation (RAG)…VibeTraining-Bench: Data-Trainer-Harness Co-Design for Iterative System Improvement
Recursive self-improvement (RSI) requires agents to diagnose system bottlenecks and coordinate improvements across training and inference.An Empirical Study of Parameter-Efficient Fine-Tuning for Mixture-of-Experts LLMs
Parameter-efficient fine-tuning (PEFT) methods are critical for adapting large models under resource-constrained settings.Bridged Self-Distillation for On-Policy Learning
On-policy distillation can transfer the reasoning capabilities of a strong teacher to a smaller student.How Benchmark Graph Construction Shapes Multi-Hop KG Navigation: A Controlled Audit of PathQuestion and PathQuestion-Large
Multi-hop knowledge-graph question answering (KGQA) benchmarks are often treated as fixed language-and-reasoning tasks, but for navigation-based methods, difficulty also depends on the graph in…LaMER: Language-Conditioned Multi-Expert Routing for Weakly Supervised Referring Expression Comprehension
Referring Expression Comprehension (REC) localizes the image region described by a natural language expression, underpinning applications such as autonomous navigation, human-robot interaction, and…Expert L2 English Speech Evaluation on a Gaming PC: Accuracy, Fluency, and Prosody with XLS-R, XEUS, and PhoneticXeus
oollim is a locally executable system for evaluating L2 English pronunciation accuracy, fluency, and prosody on a consumer computer.Where Does Rhetorical Distortion Live? A Multi-Platform, Multi-Language Measurement of Influencer Content
Much of the manipulation in online influencer content is not false but disproportionate: an accurate fact is framed as historic, urgent, or catastrophic.Co-Evolving Policy and Reward for Code Generation: A Mechanistic Study of Single-Adapter Drift
Reinforcement learning with verifiable rewards (RLVR) is particularly well suited to code generation because generated programs can be objectively evaluated through execution.Self-Consistency of Entity Sets and Citations in a Deep Research System under Repeated Prompting
Deep research agents (DRA) are commonly used for open-ended synthesis tasks where closed-world QA benchmarks are poorly suited to characterize relevant aspects of performance.Governing the Rule Space. Ruliology, Computational Irreducibility and Anticipatory Governance of Quantum Technologies
The regulation of emerging technologies is often conceived as a strategy of catching up, because legislators arrive too late.SNO-PI: Label-Scarce SNOMED CT Entity Linking with Pre-trained Encoders and Curated Terminology Knowledge
We present SNO-PI, the top-performing submission on SNOMED International's SNOMED CT Entity Linking Benchmark as of 18 October 2026.HarnessRL: Co-Evolving Policies with a Just-in-Time Training Harness for Open-Ended RL
Rubric-based reinforcement learning (RL) trains policies for open-ended generation with natural-language criteria as rewards, but these rewards can become ineffective as training progresses.The Geometry of Agent Memory: Forecasting Retrieval-Bound Memory Failures, and Characterizing Its Limits
We study whether geometric relationships between conversation turns, memory texts, and queries can predict answer correctness before generation.WenSight-Decision-1 for Direct Decisions under E-commerce Rules
E-commerce decisions require applying natural-language rules to the available case evidence.Label Authority in Speech Emotion Recognition: Intended and Perceived Ground Truth in Emozionalmente
Ground truth in Speech Emotion Recognition (SER) is often presented as a single categorical fact, yet an acted emotion and the emotion heard by listeners describe different aspects of the same…TeleASR-Amharic: A Benchmark for Robust Amharic ASR in Realistic Telecommunication Conditions
The real-world usability of ASR models remains challenging, particularly for low-resource languages, where existing speech resources are often collected in clean studio environments.Training Negatives Still Matter After Calibrating Answerability Readouts
Answerability readouts predict whether a passage supplies an answer, but whether calibration can compensate for removing negative passages during training remains unclear.AfriGuard: Safety and Robust Refusal For African Languages
Large language models (LLMs) designed to interact with users are typically trained to respond appropriately to harmful or toxic content.Strength Controls Change How Interventions Compare Across Sparse Autoencoder Codes
Changing sparse autoencoder (SAE) codes can alter both intervention direction and magnitude, so comparing intervention effects requires a strength control specifying what is held equal.