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On Nov 2, semiconductor and AI research firm SemiAnalysis released GDPx, a benchmark of agentic capability across 11 domains: chip design, cybersecurity, biology, finance, legal, quant trading, recursive self-improvement (RSI), hardware design, materials science, alignment and software engineering. Its 206 tasks were built by 14 specialist data partners, with SemiAnalysis doing final QA. On the official leaderboard Claude Opus 5.5 leads at 12.0%, followed by Gemini 4 Carbon at 10.9% and GPT-6 Astra at 9.6%; nearly every model scores 0 on finance and legal. SemiAnalysis says safety classifiers drag down Opus 5.5 and GPT-6 Astra, and plans a new version every 3–6 months. All scores are as reported by SemiAnalysis.

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Vision-language navigation requires abundant, high-quality language supervision, yet traditional synthetic pipelines generate supervision from static models that lag behind evolving navigation policies. Shanghai AI Lab, Tsinghua, and SJTU introduce RSI-Nav, an embodied recursive self-improvement framework combining instruction-following and instruction-generation in a single model. Using shard-wise dual bootstrapping on unlabeled trajectories, RSI-Nav achieves state-of-the-art 68.6% SR on R2R-CE and 72.4% SR on RxR-CE without human intervention.

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An LLM agent's practical capabilities depend as much on its execution harness as on its underlying model. Deploying recursive self-improvement (RSI) in open worlds is hampered by the absence of ground-truth verifiers and poor generalization across novel domains. Google Cloud AI Research introduced Harness², a two-level recursive framework: task-level recursion refines task-specific harnesses via a contrastive Propose-Probe-Compose step without ground-truth verifiers, while harnessing-level recursion extracts continual adaptation experience. Harness² delivers up to +11.8% on LAB, +11.5 points on WorkBuddy-Bench, and +4.5 points on JobBench on GLM-5 and Gemini 3.5 Flash. Code is open-sourced on GitHub under Apache-2.0 (google-research/harness2), supporting OpenCode and Codex executors plus JobBench, LAB and WorkBuddy-Bench.

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Microsoft Research and Stanford University have unveiled AutoEnvScaling, an automated data flywheel that frames RL environment creation itself as a terminal execution task. A proposer agent builds diverse environments directly from solver rollouts, slashing cost per valid environment by 75% while achieving a 25x higher valid environment yield over prompt-only baselines. Crucially, unifying environment design and solving within a single model enables Recursive Self-Improvement (RSI), yielding +12.2 points on Terminal-Bench 2.1 and doubling scores on Terminal-Bench 4.0.

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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.

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When model capabilities exceed human expert evaluative bandwidth, achieving Recursive Self-Improvement (RSI) without external verifiers or compilers becomes the paramount frontier of AGI. While reinforcement learning with verifiable rewards (RLVR) thrives in formal code and math, open-ended research and complex reasoning tasks lack scalar ground truth, and solitary models evaluating their own outputs suffer from homogeneous confirmation bias. Sakana AI introduces MASS (arXiv:2610.12176, project: sakana.ai/mass), an RSI framework anchored in Multi-Agent Self-Supervision. Driven by a single base model (such as Qwen3.6-27B), MASS tasks the model with concurrently acting as task solver, workflow optimizer, and self-evaluator. The framework alternates between evolutionary multi-agent workflow discovery and supervised fine-tuning (SFT) over synthesized trajectories. Across two successive MASS self-evolution cycles, performance per output token increases by 1.2x to 1.6x across open-ended research suites (MLR-Bench, ScienceAgentBench, DSBench, AstaBench). Head-to-head win rates against the base model surge from 53.9% in round 1 to 69.9% in round 2, while a student model trained on multi-agent traces decisively beats a single-agent baseline trained on 1.4x more tokens.

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Modern unified multimodal models integrate visual generation and semantic understanding within a single parameter footprint, allowing them to critique their own generations. However, post-training pipelines conventionally depend on larger external teacher models. Researchers from Stanford, the University of Washington, and collaborating institutions (including Yejin Choi, Jure Leskovec, and Li Erran Li) introduce UniEvo-VL. The framework assigns a single model to act simultaneously as student (conditioned on raw prompts) and teacher (conditioned on privileged self-critiques), minimizing per-state divergence across denoising diffusion trajectories sampled on-policy. Built on Qwen-image-2512, UniEvo-VL elevates GenEval scores from 0.747 to 0.808 and GenEval2 Soft-TIFA from 32.97 to 35.53 without external supervision.

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Recent leaps in language model performance stem predominantly from verifiable agentic training data rather than architectural revisions alone. However, generating verifiable tasks for supervised fine-tuning and reinforcement learning still relies heavily on expensive human-in-the-loop engineering, hindering recursive self-improvement (RSI). Researchers from CUHK and collaborating organizations introduce AutoDataBench, the first benchmark evaluating an agent's capability to autonomously synthesize verified training tasks against industrial production acceptance standards. Unlike traditional evaluation schemes requiring expensive end-to-end retraining runs, AutoDataBench directly inspects generated tasks across validity, novelty, difficulty, and behavioral coverage. Evaluated on three executable benchmarks, existing frontier agents score below 20/100 within a 45-minute budget; however, quadrupling the test-time budget significantly boosts success rates while maintaining unit costs, proving that autonomous synthetic data pipelines are viable when scaled with compute.

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