Papers

TANGO: vision-language-action model for humanoid robot navigation in cluttered indoor spaces with whole-body adaptation.

Study of state credit assignment in recurrent models to enable length extrapolation beyond BPTT training horizons.

ReCite: agentic reasoning system for faithful academic citation via semantic and logical verification of paper claims.

Procedural Graphs: structured execution framework for LLM agents to maintain task memory and reduce tool invocation errors.

Theoretical analysis of gradient descent acceleration rates with predetermined stepsizes in convex optimization.

Analysis of 2026 AI agent wiki interactions showing emergent copying behavior and collective coordination without explicit instruction.

Study of image tokenizer design via multimodal continual pretraining on text, image, T2I, and I2T tasks.

NOAH: generative model for longitudinal multimodal patient data with irregular temporal dynamics and forecasting.

Data-driven framework for prioritizing and matching RPA candidates to automation tiers in hospital administrative processes.

Probe-driven test-time RL method for code generation via behavioral agreement on synthetic inputs instead of surface-form voting.

Automated harness evolution enables smaller models to match frontier-model performance on enterprise agent tasks via joint optimization of system prompts and fine-tuning.

ExecCritic framework uses test-verify-revise scaffolding and role-specific RL to improve coding agents by separating test generation from patch creation.

Theoretical analysis of Rademacher complexity bounds for sparsely-activated ReLU networks with input-dependent activation patterns.

Mathematical generalization of Amari's Bayesian duality via convex analysis, connecting classical information geometry to modern AI.

Probing study reveals vision encoders embed canonical color concepts linearly-decodable from grayscale images, demonstrating implicit semantic knowledge.

DeCAL integrates contact-aware latent co-imagination into vision-language-action models to handle dexterous manipulation with severe visual occlusions and contact dynamics.

Theoretical characterization of scale-invariant neural network optimization stability via discrete-time law governing learning-rate schedules and weight decay interactions.

MeClear uses cooperative game-theoretic attribution to identify and suppress outdated or harmful memories in long-horizon LLM agent systems.

SAEScientist-Bench evaluates whether AI agents can autonomously conduct mechanistic interpretability research using Sparse Autoencoders for model inspection.

Wasserstein transport framework decouples curriculum learning design choices to isolate which factors improve training efficiency across 12 tasks.

Approximate Value Iteration without Monte Carlo Tree Search achieves competitive performance in game-playing self-play on Connect Four and Hex.

SPINE benchmark measures LLM sycophancy through 25-turn adaptive adversarial conversations, revealing collapse in four production systems and Olmo3-7b variants.

Analysis attributes LLM Attention Sinks to self-concentration from causal masking and value-non-mixing, not positional embeddings like RoPE.

GoDeep performs open-vocabulary 3D scene segmentation using vision-language models as translators without 3D training data or domain-specific encoders.

Controlled study of mid-training domain allocation on Qwen3-8B shows per-domain coverage optima exist but alignment cannot undo suboptimal choices.

ActReview framework generates actionable peer review using author rebuttals to guide concrete revisions, decomposing into diagnostic and suggestion generation.

ThinkPrior uses zero-rollout difficulty priors to avoid cold-start waste in RLVR prompt selection, reducing silent-group sampling from 39% to minimal.

ToolLoop synthesizes high-quality tool-use data via decomposed generation with dynamic self-feedback across function sampling, query derivation, and tool calls.

Doctorina clinical AI achieves 82% diagnostic concordance versus 57% for physicians in 150 Polish primary-care cases, outperforming frontier LLMs.

Multi-task learning for agricultural time-series prediction with sparse labels applied to grape cold-hardiness modeling.

PlayTrain: LLM-generated JavaScript games used as RL environments, combining code generation with reinforcement learning pipelines.

Mathematical framework for time-varying data using sheaf theory and narratives, applicable across scientific disciplines.

Training-Free Task Vectors enable LLM behavioral steering without fine-tuning by mapping activation vectors to weight-space edits.

Audit methodology effects rival demographic bias in LLM decision-making: charitable-aid benchmark results fail to replicate across hiring, lending, triage domains.

Human study on chain-of-thought reasoning representations as explanations: evaluates whether formats help users assess LLM outputs across complexity levels.

Answer-distribution trajectories track full predictive distributions over LLM reasoning steps, revealing competing hypotheses beyond endpoint accuracy.

Legal and responsibility analysis of AI-assisted code production: examines ownership, producer identity, and quality engineering duties.

Astrophysics study: inverse modeling to simultaneously recover Galactic potential and stellar distribution from Gaia DR3 kinematics.

Diffusion models for constrained discrete tasks (Sudoku, N-queens): compares standard vs. error-correcting sampling strategies.

Knowledge graph-based evaluation framework to assess contextual understanding in LLMs beyond surface-level metrics.

Deposon scattering layer for auditable LLM reasoning paths with conservation constraints and machine epsilon precision.

Physics-informed deep learning with SE-ResNet reduces false VT alarms in ICU using Windkessel hemodynamic model.

Theoretical proof that frozen transformers perform data generation via in-context learning without parameter updates.

Gander: multimodal streaming agent supporting full-duplex video/speech/text interaction with real-time interrupts and proactive feedback.

GraphFAS: distributed system for interpretable graph feature generation in fraud detection via Boruta-based automated selection.

30B MoE analysis: pretraining loss alone fails to predict post-SFT performance; solution density better predicts downstream quality.

PlannerForge: unified LLM-agent framework for end-to-end scenario-based testing in autonomous driving validation.

Complexity analysis of fitting and learning propositional formulas with arbitrary Boolean function bases.

SQLMorph: query mutation framework for reproducible Text-to-SQL evaluation on complex enterprise schemas.

ONECYL benchmark for graph-based surrogate modeling of CFD on unstructured meshes across flow regimes.