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Newest papers, page 159
Byte Language Models: Scaling, Emergent Abstractions, and Information Allocation
Tokenizer-free language models remove the inductive bias of fixed tokenizers by modeling text directly as bytes, but the resulting longer sequences substantially increase computation and eliminate…Breaking the Tie: A Cluster-Aware Routing Framework for Large Language Models
With the rapid development of artificial intelligence, the emergence of various Large Language Models (LLMs) has created a rich model ecosystem.D-Loop: Looped Diffusion Drafting for Speculative Decoding
Block diffusion accelerates speculative decoding by drafting multiple tokens in one forward pass.Investigating Query-Insensitive Behavior in Spatio-Temporal Video Grounding
Spatio-temporal video grounding (STVG) aims to localize objects or events described by natural language queries in both space and time.Differentiable Bit-Widths: Co-optimizing Pruning and Quantization via SVD for Ultra-Efficient LLM Compression
SVD-based pruning and quantization have recently emerged as a promising strategy for the ultra-efficient compression of large language models.LightMTP: Lightweight Latent Multi-Token Prediction
Next-token prediction (NTP) is the standard pretraining objective for large language models, yet it provides an explicit training signal only for the immediate next token, which can lead models to…Backdooring Sparse Autoencoders
Sparse autoencoders (SAEs) are increasingly used not only to interpret language models but also to intervene on their internal representations.ROT: Rotating Hidden States towards Contextual Vectors for Hallucination Mitigation in LVLMs
Large Vision-Language Models (LVLMs) frequently suffer from object hallucination.TrustMI: Causally controlling how assistants trust their users
Large Language Model (LLM) assistants routinely decide whether they can trust users and third parties whose competence, intentions, and integrity they cannot verify.Attention Tax, Handoff Tax: A Stylised Model of When Multi-Agent LLM Systems Help
Recent work on multi-agent LLM systems reaches sharply different conclusions: some results show that a single agent with the same information and compute should dominate a delegated system, others…Cross-Lingual Transferability of Training Data Extraction Attacks to Recover Memorized PII
The robustness of Personally Identifiable Information (PII) protection in Large Language Models (LLMs) is a critical concern, yet the risks associated with cross-lingual data extraction remain…From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation
Realistic environment replicas are increasingly valuable for training and evaluating LLM agents, yet the original systems may be inaccessible or impractical to reproduce.Efficient Test-time Adaptation through Candidate Verification and Divergence Shifts
Vision-language models (VLMs) achieve strong zero-shot transferability but remain vulnerable to target-domain shifts at inference time.Introducing Code-Switched Contexts to Cognitively-Inspired Bilingual Model Training
During language acquisition, bilingual children are regularly exposed to code-switched input and use it as a cognitive scaffold to accelerate vocabulary growth and cross-linguistic syntactic mapping.MS-Exam-Gen: Source-Grounded Benchmark Construction for Evaluating LLMs on Textual Multiple Sclerosis MRI Knowledge
Biomedical large language model (LLM) evaluation requires auditable assessment of narrow, evolving, source-grounded subspecialty knowledge.Anosognosia in LLMs: Probing Self-Awareness of Quantized Computational Substrate
Can LLMs recognize degradation in their own computational substrate?What Does It Cost to Simulate a Quantum Sentence Classifier? An Energy and Compute Perspective on Near-Term QNLP
Near-term quantum natural language processing (QNLP) experiments often run on classical simulators, so simulator cost is part of the field's practical compute burden, yet accuracy tables do not show…Judged Useless, Queried Anyway: Tool-Using Agents Rarely Turn Their Own Evidence Judgments into Stopping Decisions
An agent whose tool keeps returning nothing useful should stop relying on it.Do Small Language Models Learn to Negotiate? A Controlled Scaling Study of RL-Trained Sellers
LLM agents are starting to own the full customer experience.Cross-lingual Calibration of Pre-Generation Success Probes for Multilingual LLM Routing
Pre-generation success probes estimate response correctness from a language model's hidden activations before decoding, enabling cost-aware routing.