AI Coding Live News

Three new architectures advance 4D perception, 3D PBR texturing, and real-time autoregressive video diffusion: Gen4P (arXiv:2610.22371) repurposes a video generator into a single-pass feed-forward 4D perceiver without iterative denoising; HUST and Huawei's SUPRA-TEX (arXiv:2610.22352) reaches 32.08 dB Shaded PBR PSNR on DTC-Tex; and HKUST's ForcingBooster (arXiv:2610.12625) cuts patch denoising NFE by 50.19% for a 1.30x end-to-end speedup across Wan2.1 14B and LongLive.

Zoom

Off-policy data in reinforcement learning with verifiable rewards (RLVR) causes severe policy gradient bias during asynchronous generation and response reuse. Researchers from UIUC and HKUST introduce Proximal KL, theoretically proving that full future KL credit renders the exact proximal optimizer the unique stationary policy without requiring full-response weights or fitted critics. Evaluated across Qwen3-1.7B and 4B models, Proximal KL outperforms GRPO on 8 math benchmarks by up to 2.58% while completely averting training collapse.

Zoom

Empowering AI agents to autonomously achieve Recursive Self-Improvement (RSI) through ongoing environmental interaction is a cornerstone of artificial general intelligence. However, practical RSI pipelines are severely constrained by two fundamental bottlenecks: scarce and expensive ground-truth feedback, and the degradation of predictive world models as evolving policies induce out-of-distribution drift. Researchers from HKUST, Shanghai AI Laboratory, Peking University, and CUHK introduce RSI-Anything (arXiv:2610.13912, code: github.com/Gen-Verse/RSI-Anything), a domain-general framework unlocking recursive self-improvement via predictive world models. Built upon the JEPA-Anything architecture, RSI-Anything establishes a disciplined six-stage paradigm: Propose → Predict → Select → Evaluate → Learn → Retain. By enforcing rigorous compatibility and safety gates governing policy-world model retention, the system continuously converts interaction logs into persistent capability upgrades. Across diverse domains, RSI-Anything drives substantial performance gains: boosting automated code repair success from a 26.3% baseline to 31.2% (+4.9 points), reducing GUI navigation action footprints by 39.4%, more than doubling laboratory planning scores from 21.80% to 46.01% over five iterations, and achieving a 96.15% chemical yield, with codebase fully open-sourced.

Zoom

LLM-guided evolutionary algorithms (such as AlphaEvolve) have achieved breakthrough results in computational optimization, including circle packing and systems tuning. However, existing methods optimize strictly over iteration counts rather than fiscal expenditure, burning dozens of dollars per task. Researchers from NUS, Stanford, and HKUST present FrugalEvo (arXiv:2610.03675), a cost-aware program evolution framework maximizing gain per dollar. FrugalEvo implements an asymmetric dual-LLM division of labor: a powerful, higher-cost LLM explores high-level solution concepts, while a cheap, high-speed LLM generates and iteratively refines concrete implementations. Additionally, FrugalEvo engineers prompt harnesses to maximize prefix KV-cache reuse across evolutionary generations. Across 10 mathematical optimization problems and ALE-Bench-Lite suites, FrugalEvo matches or outperforms OpenEvolve, EvoX, and SwarmResearch. On circle packing, FrugalEvo achieves SOTA performance using GLM-5.3/Flash for only $0.55 and GPT-5.6 Terra/Luna for $1.68—slashing costs by up to 99% compared to conventional $50 multi-agent baselines.

Zoom

Group Relative Policy Optimization (GRPO) forms the reinforcement learning foundation for reasoning models like DeepSeek-R1 by normalizing group rewards to derive advantage estimates. However, practical LLM alignment requires balancing multiple competing rewards, such as code execution correctness, format compliance, tool-calling precision, and safety. Standard GRPO computes advantage normalization across aggregate variance, causing large-scale, highly correlated reward components to overshadow critical fine-grained signals. HKUST researchers propose CorrGRPO (Correlation-Normalized GRPO), which normalizes reward covariances into Pearson correlation coefficients. Across code generation, tool invocation, and agent safety benchmarks spanning 0.5B to 8B models, CorrGRPO systematically outperforms standard GRPO.

Zoom