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Newest papers, page 79
Mitigating Reward Hacking in Preference Reward Models with Evolving Penalties
Reinforcement learning for language models has moved from preference reward models to verifiable rewards, which sustain much longer optimization.RouterWorld: Scaling Conversational AI with Latency-Aware LLM Routing
Conversational AI systems built on Large Language Models (LLMs) require routing mechanisms that balance answer quality and response latency at scale.A Fisher-Geometric View of Multi-Teacher On-Policy Distillation
Consolidating specialized foundation models into a unified generalist is a central goal of post-training.ReAnchor: Reusing Rejected Verifier States for Diffusion Drafting
Block diffusion drafters accelerate speculative decoding by generating multiple draft tokens in a single forward pass.Breaking Diffusion: Parallel Reasoning with Draft Refinement
Drafting an answer allows a reasoning model to reconsider each decision in the context of the others.Beyond Attack Success Rate: Cost-Aware Metric for Evaluating Iterative LLM Jailbreaks
Attack success rate (ASR) evaluates jailbreaks against large language models without accounting for repeated calls to the victim model.BERT Is Already a Denoiser: Cheaply Unlocking Iterative Generation in Masked Language Models
Masked language models (MLMs) such as BERT are typically used for representation learning despite their denoising pretraining.Conducting Reliable Keyword Estimation with Unreliable ASR in Police Audio Data
Extracting insights from large audio data sets using human transcription is expensive and time-consuming.When the harness decides success: state that cannot be re-observed
Agent harnesses decide what a language model sees across turns: they drop old turns, summarize them, or replay the model's own earlier reasoning.From Outcomes to Components: A Counterfactual Evaluation Framework for LLM-Proposed Repairs
A repair action proposed by a large language model (LLM) specifies which records to change and how to change their values.The Development of Sycophancy in Language Models: Tracing Vulnerabilities from Pretraining to Post-Training
Language models can change a committed answer to accommodate a user's opinion or pushback without new evidence.SpatialScout: Can VLM Agents Actively Explore and Map 3D Scenes?
Spatial intelligence requires agents to actively acquire visual evidence and integrate observations into a coherent representation of the three-dimensional world.Which Model for Which Role? Cost-Efficient Pareto Recommendation for LLM Agentic Workflows
LLM agentic workflows increasingly employ different models for specialized roles.Intra-Rubric Reward Divergence: Mitigating Rubric Hacking through Latent Dimension Recovery and Balancing
Rubric hacking limits both performance and training robustness in rubric-based reinforcement learning.Parallel Language Models via Variational Decoding
Autoregressive language models emit one token per forward pass.Hierarchical Token Merging with Recursive Autoencoders for Unsupervised Grammar Induction
Hierarchical abstraction is a fundamental principle across many data modalities, enabling complex sequences to be represented compositionally at multiple levels.HoloCosm: Beyond Single-Metric Benchmark Compression Across Multiple Metrics
Benchmark compression reduces evaluation cost by selecting a small set of samples.Copying Under Repetition: From Prefix Matching to Position Hashing
Frontier LLMs can copy random text almost flawlessly, yet they often overrun or truncate long blocks of repeated characters.EpiLens: Lightweight RAG for Epidemiological Knowledge Acquisition
To prepare for future outbreaks, researchers need to know what studies have been done and what data they use.recAMPLIFY: Large-Scale Pretraining of Recursive Protein Language Models
Protein language models (pLMs) are powerful tools for protein annotation and mutation-effect prediction, but progress has largely depended on scaling model size and data, making them expensive and…