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Newest papers, page 106
VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement
Embodied visual tracking requires a robot to identify a designated target from visual observations and control its own motion to follow that target through an environment.Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems
Best-of-N sampling is a simple inference-time scaling strategy for large language models, yet its behavior under incomplete reward models remains poorly understood.Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities.Beyond Corrected Actions: Training LLM Agents on Textual Critiques
Large language models (LLMs) are increasingly used for multi-turn agentic tasks that require interactions with users and environments over extended sequences of actions.Evaluating Compressed Memory: Dependence, Utility, and Content Benefit
Does a language model answer better because its compressed memory contains useful document information?Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback
User feedback enables LLMs to refine reusable personalized skills, but often mixes persistent preferences with request-specific corrections.The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation
Identification and ranking of salient propositions in documents is indirectly involved in many AI tasks, but few papers define proposition salience scores clearly, make diverse data available, or…ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies
Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still underrepresent much of the linguistic long tail.KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms?
Korean neologisms arise through diverse mechanisms, a demanding test of language models beyond lexical memorization.Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers
Vision-language models must reconcile conflicting text and images.ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models
Diffusion large language models (dLLMs) have advanced rapidly, yet strengthening their reasoning through reinforcement learning (RL) remains an open challenge.How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs
Legal reasoning is a fundamental capability for large language models (LLMs) to complete legal AI tasks such as legal judgment prediction.More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training
Native multimodal pre-training trains a single model from scratch on text and multimodal data, enabling it to understand both language and visual inputs.ActICL: Post-Training for Agentic Multimodal In-Context Learning
Multimodal in-context learning (ICL) is an important capability of multimodal large language models (MLLMs).What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities
Post-training often improves the performance of large audio-language models on audio understanding and reasoning benchmarks.AgriJev: Efficient Agricultural Candidate Scoring with Multi-Scale Adapted Models
Agricultural systems make decisions over heterogeneous entity types, textual judgments, and knowledge-question options.Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy
Streaming Inverse Text Normalization (ITN) is vital for converting spoken-form outputs from streaming Automatic Speech Recognition into formatted written text.Structure-Preserving Document-Level Data Augmentation via Discourse Parsing
Data augmentation (DA) plays a vital role in improving model performance in low-resource or limited-supervision scenarios within Natural Language Processing (NLP).One Block Ahead: Promoting Sampler State in Diffusion Language Models
Block-diffusion language models denoise a whole block of token positions in parallel so each forward pass can emit many tokens.Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following
Verifiable instruction-following benchmarks emphasize output-constraint satisfaction but can overlook whether responses meaningfully answer the underlying request, creating an evaluation blind spot.