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Newest papers, page 95
LogicVal:A Logic-Formalized Benchmark for Constraint Satisfaction in Text-to-Image Generation
Text-to-Image (T2I) generative models have made significant progress in producing high-fidelity images from natural-language prompts.World of SWECraft: Agent Collaboration at Scale via Simulated Software Worlds
As teams of AI agents take on larger projects and long-standing open problems, agent collaboration offers another dimension for scaling inference-time compute.Location, Location, Generation: Where You Ask Matters for Generative Engine Optimization
Generative search engines answer local and commercial questions with a written answer and a few cited sources.Coopshift: Post-Training Amplifies The Memory Curse In LLM Cooperation
How does post-training change cooperation during repeated interaction?OR-License: Evaluating LLM Agents as Real-World Operations Research Practitioners
Can large language model (LLM) agents deliver expert-level operations research (OR) solutions directly from original client materials, without prior expert reformulation?Cache2LoRA: Understanding and Improving Context Internalization
to-LoRA methods encode documents as low-rank parameter updates, allowing language models to answer without retaining the source text in their decoding context.Joint, Decoupled, or Agent: When Each Multimodal Alignment Paradigm Wins
Jointly pretrained multimodal encoders are a common way to combine modalities, while alternatives integrate their information during downstream training or inference.Do LMs Know What Variables Do? Interpreting Code Representations via Variable Roles
Variable roles describe recurring behaviors such as accumulation, traversal, and retaining the best candidate encountered.Prefix-Tuning for Arbitrary Output Sequences on Pretrained Transformers
Prefix-tuning is a parameter-efficient fine-tuning method for large language models (LLMs) that steers model outputs by prepending task-specific prefix representations to the input sequence.Inside the Loop: A Mechanistic Study of Weight-Tied Transformers on Depth-Bound Algorithmic Tasks
Solving algorithmic tasks with many sequential steps typically requires scaling up transformer depth or chain-of-thought length, incurring unfavorable parameter and inference-compute costs…Continual Test-Time Learning with Evolution Strategies
Large language models increasingly operate in open-ended environments, where they are expected to continually adapt to evolving tasks and distributions, often without access to external supervision.Preservation and loss of algorithmic structure in experimental diffusion of joint-action norms
Everyday norms of social interaction - e.g. norms of sharing space, taking turns, or queuing - are essential to large societies, offering structured procedures for common interactions.CoDA: A Human-LM Collaborative Codebook Development Assistant for Domain-Specific Text Annotation
We present CoDA, an open-source human-LM collaborative codebook development assistant that helps domain experts develop annotation guidelines for annotating new variables in unstructured text with…Pronouncing Hard Words: Phoneme Adaptation of a Pretrained Conversational Text to Speech Model
Voice agents built as cascaded automatic speech recognition (ASR)->large language model (LLM)-> text-to-speech (TTS) pipelines increasingly rely on conversational TTS: streaming, context conditioned…Prosodic Structure as a Critical Component in Phonemic Language Modelling at BabyLM Scale
Human children learn language from continuous, prosodically structured speech long before they can read, yet almost all language models train on orthographic text.Multi-author style detection with stylistic embeddings
We investigate the use of Large Language Models (LLMs) in multi-author style change detection.Reasoning with Context Management and External Memory
Reasoning models maintain information about the status of their reasoning process through in-context memory.Language Models are Lazy Poets: Creative Mode Collapse at Scale
While aligned large language models (LLMs) can successfully complete many creative tasks, their outputs tend to be highly self-similar.Analyzing Repetition and Interference Effects in Memory-Constrained User Simulators
User simulators can help evaluate and train AI systems to interact with humans at scale.Preserving Reasoning Diversity in Reinforcement Learning with Verifiable Rewards via Adaptive Replay
Reinforcement Learning with Verifiable Rewards (RLVR) improves the reasoning capabilities of large language models, but often reduces the diversity of their reasoning behaviors.