FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model
FurnitureVLA enables long-horizon bimanual furniture assembly via vision-language-action models with VR teleoperation and progress-enhanced training.
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FurnitureVLA enables long-horizon bimanual furniture assembly via vision-language-action models with VR teleoperation and progress-enhanced training.
Audit exposes reliability issues in coding-agent benchmarks (GSO, SWE-Perf, SWE-fficiency): runtime instability, scoring artifacts, selection bias.
Cloudflare is giving AI companies until September 15 to separate web crawlers used for search from those used for AI training and agents, or risk being blocked by default on many publisher sites.
Cartridge distillation detects stealth biases in LLMs by exposing preferential signals in logit distributions invisible to text-based analysis.
TiRex-2 extends xLSTM-based time series foundation model to multivariate forecasting with streaming and covariate dependencies.
GPU-parallel linearization error bounds for robust optimal control of nonlinear and neural network dynamics enable real-time planning with certified constraints.
World from Motion reconstructs freely renderable dynamic 3D Gaussian scenes from monocular video via conditioned video models and synthetic artifacts.
Empirical comparison of quantum vs. classical ML across seven model pairs finds insufficient evidence for quantum ML computational advantages.
Constraint programming and scheduling method optimizes resource utilization in autonomous labs for metal-organic framework synthesis experiments.
Neural Certificate Pricing exploits polynomial verification in combinatorial optimization by training neural networks to predict dual prices for certificates.
Adversarial generator-discriminator framework augments RLVR with learned style discriminator to prevent diversity collapse and reward hacking in LM training.
QuasiMoTTo uses quasi-Monte Carlo sampling to reduce redundancy in parallel test-time inference scaling for language models.
Reinforcement learning (RL) is central to aligning language models, from reinforcement learning with human feedback (RLHF) within AI assistants to newer... Reinforcement learning (RL) is central to aligning language models, from reinforcement learning with human feedback (RLHF)…
Decision-aware training augments energy score objectives with differentiable decision loss to optimize sample-based generative models for downstream task costs.
Diffusion-GR2 uses block-diffusion decoder for parallel position decoding to accelerate generative reasoning re-rankers with minimal accuracy loss.
Learned 3D Gaussian representation compresses structured and unstructured volume data more efficiently than INRs via explicit geometry encoding.
Bilingual evaluation protocol for cross-lingual speaker verification on Iberian languages isolates language effects from speaker variation.
US lifts curbs on Anthropic’s advanced Fable and Mythos models.
Adversarial pragmatics benchmark for LLM safety evaluation addresses instruction conflict, embedded commands, and policy ambiguity beyond binary pass/fail.
AGC-Bench unifies 78 datasets spanning multiple domains into HELM-standardized artificial general creativity benchmark for LLM evaluation.
ER-JEPA applies hierarchical self-supervised learning to ECG time-series analysis with limited labeled data.
IMPFM framework for sample-efficient online feedback-driven search via multi-particle flow-maps in high-dimensional spaces.
Agent Skill Supply Chains framework applies software dependency management to LLM agent skill composition and provenance tracking.
DiscoPER framework enables open-ended autonomous scientific discovery via iterative meta-reflection and cross-finding synthesis in LLM agents.
GAIA operator learning model extends geometry-adaptive neural operators to inverse problems and boundary value problems with domain mismatches.
Log_bQuant logarithmic quantization method improves 4-bit LLM compression by adapting to non-uniform weight distributions.
ZO-Act enables zeroth-order fine-tuning of LLMs via activation-informed low-rank subspace optimization without backpropagation.
Paper proposes Muon optimizer acts as implicit residual connection during training, improving representation preservation via orthogonalization trade-offs.
University RAG-based multimodal chatbot prototype for institutional Q&A, combining LLMs with semantic retrieval for domain-specific policy queries.
FAR framework enables robots to learn from test-time failures via preference learning, improving autonomous task completion without human intervention.