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Newest papers, page 18
AlgoPilot: Cross-Paradigm Reasoning in Language Models via Strategy Selection and Guidance
Real-world problems span diverse domains, including program synthesis, symbolic reasoning, planning, and optimization, and often require fundamentally different solution paradigms.Unlocking Turing Tensor Cores: A Measured Throughput Account of From-Scratch Pre-training on a Single 22G RTX 2080 Ti
The RTX 2080 Ti (TU102) ships full second-generation Tensor Cores in hardware, yet the vendor throttles FP16 matrix multiplication at the driver level, leaving usable tensor throughput well below…XDecideBench: Evaluating Typed Decision Models and Confidence Policies Across Languages
Applications use Typed Decision Models to route and moderate text in many languages, and they often act only on decisions above a confidence threshold.Information-Theoretic Design of Sparse Autoencoder-Based Steering
Sparse autoencoder (SAE)-based activation steering is a lightweight approach for modifying the behavior of large language models (LLMs) by adding small vectors to hidden states during inference.Learning by Doing: Autonomously Compiling and Curating Agent Skills from Verified Traces
Memory systems for production LLM agents mostly store semantic memory (facts about users and domains) or episodic memory (earlier interactions).RAG-TRACE: Tracing Measurement Evidence and Diagnostic Completeness in AI-Generated ECG Reports
Retrieval-augmented generation (RAG) can introduce electrocardiogram (ECG) criteria that lack patient-specific measurement support.Latent Harness: Compiling Agent Behaviors into Post-Block Activation Shifts
Harness engineering and self-evolving agents improve language-model agents quickly, yet their gains live outside the model.FlyWheel: Attention Kernels Built for the TPU
Attention kernels on Tensor Processing Units (TPUs) often underutilize their matrix units (MXUs) because of repeated matrix operand loads, vector-intensive computation, and dependencies that leave…Towards Fine-grained and Robust Detection of Human-AI Co-authorship
The widespread adoption of large language models (LLMs) is reshaping how textual content is produced, complicating text provenance, and raising concerns for misinformation, plagiarism, and…When Should an Agent Refresh? Adaptive Index Reuse for Efficient Long-Horizon Reasoning
Language agents read code, tool output, and dialogue in contexts that grow at every step.Can Large Language Model Agents Learn by Watching Tutorial Videos?
Humans continuously acquire knowledge from carefully crafted materials such as books and tutorial videos, carrying that understanding into future tasks.See, Think, Use: Diagnosing Perception and Reasoning Failures in VLMs
We present See, Think, Use, a protocol that traces each answer of a vision-language model to perception, reasoning or the use of perception.Accounting for Irregular State Transitions through Belief Estimation in Online Medical Consultation
While online medical consultation broadens access, unprompted patient disclosures and topic shifts frequently disrupt clinician inquiry, leaving clinical states progressively revealed.TopoSteer: Topological Layer Selection for Activation Steering in LLMs
Activation steering controls large language models at inference time by adding a vector to their internal representations.Sweep Less, Steer More: Using Activation Steering Geometry for Reduced Hyperparameter Search
Representation steering methods like difference-in-means (DIM) offer a lightweight approach to controlling LLM behavior, but their effectiveness varies widely across concepts, datasets, and layers…VLMs Read Aloud: The Gap Between Recognizing and Using Visual Text
Visual-text compression renders long contexts as images and is engineered around how much of the text a vision-language model (VLM) can recover.Write Facts the Way Models Read Them: Lifting the Two-Hop Curse
A language model that learns a new fact by fine-tuning can repeat it but cannot reason with it: taught that a person is a citizen of a new country, it still cannot say, without chain-of-thought, who…The Structure of Disagreement in Emotion Annotation
Calculating inter-annotator agreement is an established practice in NLP, and disagreement-aware work increasingly retains the distribution of human judgments rather than reducing annotations to a…Revisiting LLM Homogeneity Through a Multidimensional Lens
Concerns about LLM homogeneity are typically quantified with embedding similarity, which reduces two texts to a single score.Can Large Language Models Extend Their Agentic Capabilities to Goal Pursuit in Open-Ended Multi-Turn Interaction?
Goal pursuit in open-ended dialogue requires agents to adapt their actions to interaction feedback across successive exchanges.