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@huggingface@huggingface·2h ago
🔥 Trending

Zero-Human-Label Step Accuracy Jumps from 48.2% to 74.7%: LeanPRM Grounds Process Supervision in Lean 4, While Cornell's CPLM Matches Full Attention at w=128

Tackling step-level verification in mathematical reasoning and in-context copying bottlenecks in Transformers, NYU/UCLA/Stanford researchers introduce LeanPRM (arXiv:2610.17697)—using the Lean 4 theorem prover to automatically label 152K steps across 10,634 natural-language proofs with zero human annotation, boosting Qwen3-4B step accuracy on LeanPRM-Bench from 48.2% to 74.7% (vs. 60.3% for PRM800K). Meanwhile, Cornell University's Copy-Paste Language Models (CPLMs, arXiv:2610.19247) add a lightweight Copy-Paste head (~3% compute overhead) that relieves the LM head gradient bottleneck, boosting LAMBADA accuracy by +2.5-4.0 points and allowing a w=128 sliding-window CPLM to match full-attention perplexity.

Zero-Human-Label Step Accuracy Jumps from 48.2% to 74.7%: LeanPRM Grounds Process Supervision in Lean 4, While Cornell's CPLM Matches Full Attention at w=128
⚡ Key Takeaways
  • •LeanPRM uses the Lean 4 proof assistant and a syntax-error filter to generate 152K step-level labels across 10,634 proofs with zero human annotation
  • •A Qwen3-4B verifier trained on LeanPRM reaches 74.7% step accuracy on LeanPRM-Bench (vs. 48.2% base and 60.3% with PRM800K) and improves 0.6B-8B verifiers on Hard2Verify and IMO-GradingBench
  • •Cornell's Copy-Paste Language Models (CPLMs) add a ~3%-overhead copy head that relieves the LM head gradient bottleneck, boosting LAMBADA by +2.5-4.0 points and matching full-attention perplexity with a w=128 sliding window
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@huggingface@huggingface·2h ago
🔥 Trending

ZJU's VLA-Δ Sets 98.4% LIBERO Record, SJTU/HKU's OneWM Doubles π0.5 on RoboCasa365, and CoDA Lifts Perturbed Robot Success from 1% to 76%

Three new open-source embodied AI frameworks overcome observation-action overfitting in Vision-Language-Action (VLA) policies: Zhejiang University's 2B VLA-Δ (arXiv:2610.21306, 54 upvotes) supervises explicit physical state transitions with zero inference overhead to set a 98.4% SOTA on LIBERO; SJTU and HKU's OneWM (arXiv:2610.21095, 40 upvotes) unifies causal world and action modeling to reach 36.68% on RoboCasa365 (over 2x π0.5 at 16.90%); and UIUC/Amazon's CoDA (arXiv:2610.22362) co-evolves simulation data engines and action policies via Text2potential shaping, raising LIBERO-Pro success from 25% to 84% under position perturbations and from 1% to 76% under instruction perturbations.

ZJU's VLA-Δ Sets 98.4% LIBERO Record, SJTU/HKU's OneWM Doubles π0.5 on RoboCasa365, and CoDA Lifts Perturbed Robot Success from 1% to 76%
⚡ Key Takeaways
  • •Zhejiang University's 2B VLA-Δ explicitly supervises physical state transitions with zero inference overhead, setting a 98.4% SOTA on LIBERO and beating π0.5 by +6.4 points under language perturbations
  • •SJTU and HKU's OneWM unifies causal world and action modeling, achieving 36.68% overall success on RoboCasa365 (2.17x π0.5) and leading OOD LIBERO-Plus at 59.27%
  • •UIUC/Amazon's CoDA co-evolves simulation engines and policies via Text2potential shaping, raising LIBERO-Pro success from 25% to 84% under position perturbations and from 1% to 76% under instruction perturbations
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@SemiAnalysis_@SemiAnalysis_·3h ago
🔥 Trending

SemiAnalysis Breaks Down 1.3B Tokens/$ AgentX Record: vLLM + Inferact Disaggregated Serving Beats Aggregated B300 by 4x, While Cline Highlights Multi-Harness RL

SemiAnalysis published the architectural breakdown behind the 1.3B tokens per dollar TCO record on AgentX: vLLM and Inferact utilized Prefill/Decode disaggregated serving at hyperscaler volume pricing to achieve 4x better cost-efficiency than an aggregated NVIDIA B300 configuration. Concurrently, open-source coding agent Cline reported that DeepSeek-V4.1-Flash-1017 beats GPT-5.6-Sol on Terminal-Bench at ~17x lower cost (+35% over prior V4.1-Flash) purely by scaling RL training across multiple coding agent harnesses.

SemiAnalysis Breaks Down 1.3B Tokens/$ AgentX Record: vLLM + Inferact Disaggregated Serving Beats Aggregated B300 by 4x, While Cline Highlights Multi-Harness RL
⚡ Key Takeaways
  • •SemiAnalysis confirms vLLM and Inferact achieve 1.3B tokens per dollar TCO on AgentX via disaggregated serving—4x better than an aggregated NVIDIA B300 configuration
  • •Cline reports that DeepSeek-V4.1-Flash-1017 improves prior V4.1-Flash Terminal-Bench performance by 35%, beating GPT-5.6-Sol at ~17x lower cost
  • •The 35% agentic coding leap was achieved without base algorithmic changes by scaling reinforcement learning directly across multiple coding agent harnesses
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OpenCode@opencode·4h ago
🛠️ Tooling

OpenCode confirms breach: distillation-linked incident hit ~1.4% of Console users; remediating with OpenAI, Anthropic, Google

On 2026-10-17 OpenCode disclosed an overnight breach tied to a large-scale distillation attack affecting about 1.4% of Console users. Affected accounts were emailed; no email means not impacted; only users with external providers linked. Remediation is ongoing with Anthropic, OpenAI and Google. No official blog or GitHub advisory yet; the status page listed no dedicated security incident.

⚡ Key Takeaways
  • •Official: ~1.4% of Console users; no email means not impacted
  • •Scope: subset with external providers linked
  • •Remediating with Anthropic, OpenAI, Google; details limited
  • •If keys were saved in Console, rotate at each vendor and review usage
  • •No official key count, root cause, or confirmed abuse published
Read details→
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Anthropic (Claude Code team)@AICoder·6h ago
🔥 Trending

How the Claude Code team reviews code: ~90% Claude-written, merged PRs more than doubled June–Sept while revert share fell over 40%

An Oct 15 Anthropic engineering post details layered review on the Claude Code repos: pre-PR self-verification plus fresh-context subagent review, parallel bug-hunting agents with voting verifiers, blocking security review, risk-tiered human approval (0/1/2 people), merge queue, flags and fast rollback. All metrics are the team's internal figures.

How the Claude Code team reviews code: ~90% Claude-written, merged PRs more than doubled June–Sept while revert share fell over 40%
⚡ Key Takeaways
  • •Merged PRs to Claude Code more than doubled June–Sept while the revert share fell over 40% (internal figures)
  • •Anthropic monorepo changes per day grew from ~2,500 in May to 30,000+ in September
  • •Convergence fixes: one-round pass 65%→78%, 5+ rounds 8.9%→1.5%, mean reviews to pass 2.08→1.32
  • •After the Code Review research preview, internal PRs with substantive comments rose from 16% to 54%
  • •Risk tiers set human approvals at 0/1/2; an agent's approval never counts as a person's
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@PaddlePaddle@PaddlePaddle·6h ago
🔥 Trending

When Remote KV Cache Is Slower Than GPU Recompute: Sharded LMCache Cuts p99 from 83.32s to 0.67s on 8xB300, While Baidu VHA Halves KV Cache and Beats MLA

Addressing KV cache bottlenecks in long-context LLM serving, a new production study on 8xB300 nodes serving GLM-5.3 (arXiv:2610.21500) reveals that unsharded remote KV caches inflate hit-heavy p99 TTFT to 83.32s, and resolves it via rank-sharded asynchronous LMCache loading—boosting GET bandwidth 6.3x to 10.76 GB/s and slashing p99 to 0.67s (+34% throughput). Meanwhile, Baidu PaddlePaddle open-sources Virtual Head Attention (VHA, arXiv:2610.17959), folding linear Q-Premix and Postmix into standard GQA-2 weights at inference to halve KV cache while beating MLA (+0.70%) and GQA-4 (+2.60%) on a 30B-A3B MoE and lifting 8K-128K RULER from 73.0 to 84.4.

When Remote KV Cache Is Slower Than GPU Recompute: Sharded LMCache Cuts p99 from 83.32s to 0.67s on 8xB300, While Baidu VHA Halves KV Cache and Beats MLA
⚡ Key Takeaways
  • •On 8xB300 nodes serving GLM-5.3 with PCP8, sharding LMCache across ranks and adding async loading boosts GET bandwidth 6.3x to 10.76 GB/s, cutting hit-heavy p99 TTFT from 83.32s to 0.67s (+34% throughput)
  • •Baidu's Virtual Head Attention (VHA) folds linear Q-Premix and low-rank Postmix directly into W_Q and W_O at inference, reducing to a zero-overhead GQA-2 architecture with half to a quarter of the KV cache
  • •On a 500B-token 30B-A3B MoE, VHA outperforms both MLA (+0.70%) and GQA-4 (+2.60%), and boosts 8K-128K RULER on a 10B-A1B MoE from 73.0 (GQA-4) to 84.4 using only 2 KV heads
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@huggingface@huggingface·6h ago
🔥 Trending

Ending Coding Agent Reward Hacking: CMU's Weak-to-Strong Verifier Hardening Adopted by Terminal-Bench, While Recursive Environment Scaling Boosts SFT by up to 14.6 Points

Tackling reward hacking and benchmark saturation in coding agent environments, CMU researchers introduce Scalable Weak-to-Strong Verifier Hardening (arXiv:2610.15924)—combining an isolated verifier sandbox with an iterative hacker-fixer loop that slashes exploit success across Terminal-Bench, KernelBench, and SETA, and is now officially adopted by Terminal-Bench. Complementing this, Recursive Environment Scaling (RES, arXiv:2610.16397) recursively mutates execution dependencies across 294 tasks—exposing a 57.14-77.14 point drop on Claw-Eval-Live while boosting equal-budget SFT performance by 7.00-14.61 percentage points across all nine model-benchmark pairs.

Ending Coding Agent Reward Hacking: CMU's Weak-to-Strong Verifier Hardening Adopted by Terminal-Bench, While Recursive Environment Scaling Boosts SFT by up to 14.6 Points
⚡ Key Takeaways
  • •CMU's weak-to-strong verifier hardening combines an isolated sandbox with an iterative hacker-fixer loop across Terminal-Bench, KernelBench, and SETA, and has been adopted by the official Terminal-Bench repo
  • •Recursive Environment Scaling (RES) mutates information access, resource relations, and execution dependencies across 294 tasks, dropping model success on Claw-Eval-Live by 57.14-77.14 percentage points
  • •With equal trajectory counts, SFT on RES-scaled environments beats original-trajectory SFT across all 9 model-benchmark pairs by +7.00 to +14.61 percentage points
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@huggingface@huggingface·6h ago
🔥 Trending

Humans Score 100% While GPT-5.6-Sol Hits Only 17%: RoTA-Bench Exposes 3D Interactive Control Gaps, and OpenSAE Pairwise Steering Lifts Fidelity to 0.779

Two top-trending Hugging Face papers dissect the limits of VLM 3D interactive reasoning and LLM mechanistic interpretability: Xiamen University's RoTA-Bench (arXiv:2610.17665, 200 upvotes) evaluates closed-loop 3D-to-2D projection alignment across 674 tasks, finding that humans achieve 100% success while GPT-5.6-Sol reaches only 17%. Meanwhile, UW-Madison's OpenSAE study (arXiv:2610.21261, 104 upvotes) introduces SAE Additivity Residuals (SAR) across 18 model/behavior settings, showing that modeling pairwise feature interactions boosts multi-feature steering rank fidelity from 0.5874 to 0.7790 and cuts ordering error from 0.1444 to 0.0606.

Humans Score 100% While GPT-5.6-Sol Hits Only 17%: RoTA-Bench Exposes 3D Interactive Control Gaps, and OpenSAE Pairwise Steering Lifts Fidelity to 0.779
⚡ Key Takeaways
  • •On RoTA-Bench's 3D-to-2D interactive projection alignment tasks, human participants achieve a 100% success rate whereas GPT-5.6-Sol succeeds on only 17%, worsening 3D pose while improving 2D overlap 19.4% of the time
  • •Removing interaction history causes 96.8% of VLM rotation actions to degenerate into coarse 30-degree steps, underscoring the necessity of multi-turn visual feedback
  • •UW-Madison's OpenSAE framework introduces SAE Additivity Residuals (SAR) across 18 settings, showing pairwise feature interaction modeling raises multi-feature steering rank fidelity from 0.5874 to 0.7790 and cuts ordering error from 0.1444 to 0.0606
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@GoogleDevs@GoogleDevs·6h ago
🔥 Trending

Google AI Studio Opens Gemini 4 Argon in Build & Playground for Pro Users; Transformers v5.20.1 Adds Native Reflection AI Beam and Meta 816B Muse Spark Open

Google AI Studio launched Gemini 4 Argon access in AI Studio Build and Playground for Google AI Pro subscribers, while Hugging Face Transformers v5.20.0 and v5.20.1 added native support for Reflection AI's frontier coding model Beam, Meta's 816B-parameter (42B active, 1M context) multimodal agentic coding model Muse Spark Open, InclusionAI's Ling 3.0 Flash, and OpenBMB's MiniCPM-V 4.7.

Google AI Studio Opens Gemini 4 Argon in Build & Playground for Pro Users; Transformers v5.20.1 Adds Native Reflection AI Beam and Meta 816B Muse Spark Open
⚡ Key Takeaways
  • •Google AI Studio opens Gemini 4 Argon in AI Studio Build and Playground (https://ai.studio/build) for Google AI Pro subscribers
  • •Hugging Face Transformers v5.20.1 natively integrates Reflection AI's token-efficient coding model Beam and fixes DeepGEMM Mega MoE under expert parallelism
  • •Adds native support for Meta's 816B-parameter (42B active, 1M context, BF16/NVFP4) omni-modal coding model Muse Spark Open and InclusionAI's KDA+MLA hybrid Ling 3.0 Flash
Read details→
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Anthropicgithub.com·8h ago
🚀 Release

Claude Code 2.1.302 turns on fork subagents by default across claude -p, Agent SDK, desktop and VS Code; standalone Claude Design closes Dec 14

Claude Code v2.1.302 (Oct 16) makes fork subagents the default in claude -p, the Agent SDK, desktop and VS Code, starts permission-mode-less headless sessions on third-party providers in default mode, and adds CLAUDE_CODE_AUTO_MODE_MODEL. It also flags that standalone Claude Design (claude.ai/design) closes on Dec 14, 2026; design systems must be migrated to Claude Artifacts, while chats and comments are not carried over.

⚡ Key Takeaways
  • •Fork subagents now default-on in claude -p, Agent SDK, desktop and VS Code; disable with CLAUDE_CODE_FORK_SUBAGENT=0
  • •Headless sessions with no permission mode on third-party providers start in default mode (telemetry off or fresh install); --permission-mode auto keeps auto
  • •New CLAUDE_CODE_AUTO_MODE_MODEL picks the client-side auto mode classifier model; prompt/agent hooks capped at 12 concurrent per event
  • •Read tool refuses image sources over 128 MB; MCP servers on protocol 2026-07-28 that omit resultType/ttlMs/cacheScope no longer break
  • •Standalone claude.ai/design closes Dec 14, 2026; chats, comments and public links are not migrated and originals are deleted afterward
Read details→
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Anthropicanthropic.com·10h ago
🔥 Trending

Claude traces the NIST elliptic-curve seeds to lrand48 seeded with 1997–98 timestamps, settling a 25-year backdoor question

Anthropic says an internal research model recovered, in under half an hour, how the seeds of NIST P-192/224/256/384/521 were produced: lrand48 output seeded with Unix timestamps from 1997–98, not a hashed phrase. Bounty organizer Filippo Valsorda independently reproduced it in Go; the $36,864 bounty goes to ISRG. This is not a break and no action is needed.

Claude traces the NIST elliptic-curve seeds to lrand48 seeded with 1997–98 timestamps, settling a 25-year backdoor question
⚡ Key Takeaways
  • •P-256 seed = candidate #1842 after srand48(871696354) (1997-08-16 01:52:34 UTC), per Anthropic
  • •Tell: 4 of 10 seeds had the top bit clear in all twenty 32-bit words (≈1/5810 by chance), the fingerprint of lrand48's 31-of-48-bit output
  • •Prime-curve seeds use lrand48() >> 23; truncated LCG state recovered with LLL lattice reduction; all 10 init times fall May 1997–Apr 1998
  • •Bounty claimed 2026-10-15 01:07:51 UTC, tripled to $36,864 and donated to ISRG; Filippo Valsorda reproduced it in Go
  • •Still open: the B-163 seed, base points of P-224/256/384/521, and why viable candidates (e.g. P-256 #876) were skipped
Read details→
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@vllm_project@vllm_project·12h ago
🔥 Trending

vLLM and OpenRouter Launch Day-0 Serving for DeepSeek-V4.1-Flash-1017: 830K tok/s per GPU and 1.3B Tokens/$ TCO on SemiAnalysis AgentX

Following the release of DeepSeek-V4.1-Flash-1017, vLLM announced Day-0 serving optimization delivering 1.3B tokens per dollar TCO and 830K tok/s per GPU at 100 tok/s/user on SemiAnalysis AgentX (5x agentic throughput gain since Day 0), while OpenRouter launched live multi-provider routing starting at $0.10/M input and $0.50/M output tokens.

vLLM and OpenRouter Launch Day-0 Serving for DeepSeek-V4.1-Flash-1017: 830K tok/s per GPU and 1.3B Tokens/$ TCO on SemiAnalysis AgentX
⚡ Key Takeaways
  • •vLLM delivers 830K tok/s per GPU (cached + input + output) at 100 tok/s per user and 1.3B tokens/$ TCO on SemiAnalysis AgentX for DeepSeek-V4.1-Flash-1017 (5x faster since Day 0)
  • •DeepSeek-V4.1-Flash-1017 reaches 77.0% on DeepSWE v1.1 (beating Claude Opus 5 at 74.0%), 68.2% on NL2Repo-Bench, and 72.1% on Apex Bench with shorter agentic outputs
  • •Live on OpenRouter from $0.10/M input and $0.50/M output tokens, while DeepSeek's official API alias deepseek-flash remains at $0.15/M input and $0.60/M output
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DeepSeekdeepseek_ai·14h ago
🚀 Release

DeepSeek ships V4.1-Flash-1017: MIT open weights, live on web, app and API at unchanged prices, DeepSWE 77.0 (vendor-reported), plus public Search/Fetch APIs

On Oct 17 DeepSeek upgraded V4.1 Flash to V4.1-Flash-1017 across web, app and API (deepseek-flash, same call and price) and published MIT-licensed weights on Hugging Face and ModelScope. Vendor-reported DeepSWE v1.1 rises from 74.2 to 77.0 and Terminal-Bench 4.0 from 31.2 to 42.3, with shorter runtimes and outputs. It also opened Search/Fetch APIs backed by its in-house search engine at $0.75 per 1,000 calls.

DeepSeek ships V4.1-Flash-1017: MIT open weights, live on web, app and API at unchanged prices, DeepSWE 77.0 (vendor-reported), plus public Search/Fetch APIs
⚡ Key Takeaways
  • •Vendor-reported DeepSWE v1.1 77.0 (prev. 74.2), above Claude Opus 5 (74.0) and GPT-5.6 Sol (73.0) in DeepSeek's table
  • •Terminal-Bench 4.0 31.2 → 42.3 and ProgramBench 20.3 → 29.1, still behind Opus 5 (51.8 / 37.0); HLE w/o tools 42.6 vs Opus 5's 56.3
  • •Pricing unchanged: deepseek-flash peak $0.30 input (cache miss) / $1.20 output per 1M tokens, off-peak half; 1M context, 384K max output
  • •Search / Fetch APIs at $0.75 per 1,000 calls each; server-side calls via DeepSeek Harness, Claude Code and Codex are free until Oct 31, 2026 16:00 UTC
  • •MIT weights on Hugging Face in FP8 (FP4 experts); no Jinja template — use the bundled encoding.py or deepseek-recipe
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@PaddlePaddle@PaddlePaddle·14h ago
🔥 Trending

Baidu PaddlePaddle Open-Sources FlashMask-3: Hopper 3-Role Warp Specialization and Mask-Aware Distributed Attention with up to 3.71x CP Speedup

Baidu PaddlePaddle introduces FlashMask-3 (arXiv:2610.17901), combining a 3-role warp-specialized Hopper kernel with zero-communication mask localization, HCC-IPO sparsity load balancing, and mask-aware communication overlap. It boosts single-H100 TFLOPs by 45-141% over FlashMask, accelerates Context Parallelism by up to 3.71x over Megatron-Core, and speeds up 128-H100 118B-A14B MoE attention by up to 44.9%.

Baidu PaddlePaddle Open-Sources FlashMask-3: Hopper 3-Role Warp Specialization and Mask-Aware Distributed Attention with up to 3.71x CP Speedup
⚡ Key Takeaways
  • •3-role warp-specialized Hopper kernel boosts single-H100 TFLOP/s across 12 masks (4K-128K) by 45-141% over FlashMask and 34-71% over PyTorch FlexAttention
  • •Zero-communication mask localization and HCC-IPO sparsity load balancing achieve 1.80x-3.71x higher throughput than Megatron-Core CP ring attention at CP degrees 4-32
  • •Accelerates attention fwd+bwd by 24.5-44.9% and raises end-to-end 128-H100 training throughput by 7.3-11.0% on a 118B-A14B MoE model at 128K context
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mofcom.gov.cnSource·14h ago
🔥 Trending

China proposes export controls on 23 AI technologies: weights of 2T-parameter or global top-10 models restricted, open source and API services exempt

On Oct 16 China's commerce and science ministries proposed adding 40 technologies, 23 of them in AI, to the export ban and restriction catalogue. The draft restricts core technology of models with at least 10^26 training FLOPs, at least 2 trillion parameters, or a global top-10 ranking, and sets step, concurrency and tool-count thresholds for agents. Open-sourced technology is exempt, and API or SaaS services are not treated as technology exports. Temporary pre-export registration took effect the same day; comments close Nov 15.

⚡ Key Takeaways
  • •23 AI items out of 40 proposed technologies (14 prohibited, 26 restricted across AI, biomedicine and photovoltaics); comments open Oct 16 to Nov 15, 2026
  • •Restricted model thresholds (any one): at least 10^26 training FLOPs, at least 2 trillion parameters, or a global top-10 ranking; covers weights, checkpoints, source code, architecture and training or inference acceleration
  • •Agent thresholds include 20 or more sub-task steps with at least 90% average success, 60 or more concurrent agents, or 200 or more integrated tools
  • •Open-sourced or published technology is exempt; serving via API, app or web, SaaS, or lightweight on-device deployment is not a technology export, while overseas on-premise deployment is
  • •Temporary pre-export registration has applied since Oct 16, including deals already underway
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@huggingface@huggingface·15h ago
🔥 Trending

Zero-Token 5.6x Faster Spatial Intelligence: MJev-Spatial Tops HF Daily Papers Alongside CUHK JIA-Lab's CarryOn Dual-Loop Robot Harness

Tackling high latency and safety failures in spatial VLM and embodied VLA deployment, MJev-Spatial (arXiv:2610.22276, #1 on HF Daily Papers with 350 upvotes) reformulates spatial judgment as single-pass native Q/K attention matching with zero added parameters—achieving 65.64% across 12 benchmarks while running 5.6x faster than its Qwen3.5-4B base—while CUHK JIA-Lab's CarryOn (arXiv:2610.21453) wraps frozen VLA policies in a dual-loop harness that cuts safety violations from 52.6% to 6.1% and raises physical recovery success from 13% to 80%.

Zero-Token 5.6x Faster Spatial Intelligence: MJev-Spatial Tops HF Daily Papers Alongside CUHK JIA-Lab's CarryOn Dual-Loop Robot Harness
⚡ Key Takeaways
  • •MJev-Spatial reuses pretrained Q/K projections for single-pass spatial classification with zero generated tokens, running 5.6x faster than its Qwen3.5-4B base with a top 65.64% average across 12 benchmarks
  • •Teacher-forced coarse-to-fine supervision and length-aware global batching boost MJev-Spatial training throughput by 30.8% across 12.3M spatial QA pairs
  • •CUHK JIA-Lab's CarryOn dual-loop VLA harness cuts safety violations from 52.6% to 6.1% and raises physical failure recovery success from 13% to 80%
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@huggingface@huggingface·15h ago
🔥 Trending

Breaking GRPO Exploration Collapse: SERO Executable Oracle Continuations (+11.2 pts on ALFWorld OOD) and EDEN Anchored Embedding Noise (+13.3 pts on AIME 2025)

Addressing rollout homogenization and zero-variance advantage collapse in group-relative reinforcement learning (GRPO), SERO (arXiv:2610.19397) restores learner-visited states to execute multi-step oracle continuations with policy-reachability weighting—boosting 7B ALFWorld OOD success by +11.20 points to 87.50%—while UNSW's EDEN (arXiv:2610.16105) appends a single anchored Gaussian-perturbed embedding position to a fraction of rollouts, lifting Qwen2.5-Math-7B AIME 2025 Pass@256 by +13.33 points (to 66.67%) without increasing rollout budgets.

Breaking GRPO Exploration Collapse: SERO Executable Oracle Continuations (+11.2 pts on ALFWorld OOD) and EDEN Anchored Embedding Noise (+13.3 pts on AIME 2025)
⚡ Key Takeaways
  • •SERO restores learner-visited states and executes multi-step oracle continuations with policy-reachability weighting, boosting 7B ALFWorld unseen success by +11.20 points to 87.50% (95.57% seen)
  • •UNSW's EDEN appends a single anchored Gaussian-perturbed embedding position to a fraction of rollouts, beating G=32 standard GRPO using only G=16 rollouts
  • •On AIME 2025, EDEN lifts Qwen2.5-Math-7B Pass@256 by +13.33 percentage points (from 53.33% to 66.67%) while preserving diverse multi-derivation reasoning traces
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ClaudeDevsClaudeDevs·16h ago
🔥 Trending

Claude Code waits 3x less for the first token: gzip on every turn and cloud sessions that warm up as you type (p90 4.07s to 1.33s)

On Oct 16 Anthropic shared August-to-October latency gains. p90 wait before the first token, excluding model time, fell from 4.07s to 1.33s for local Claude Code sessions (about 3x) and from 16.99s to 6.00s for cloud sessions (2.8x); Claude chat and Cowork improved about 1.5x, and API p50 time to first token dropped from 1.53s to 1.01s. The main changes are default gzip for large requests since CLI 2.1.300 and cloud sessions that start when you begin typing.

Claude Code waits 3x less for the first token: gzip on every turn and cloud sessions that warm up as you type (p90 4.07s to 1.33s)
⚡ Key Takeaways
  • •Local Claude Code p90 wait before first token (excluding model time): 4.07s to 1.33s, about 3x; cloud sessions 16.99s to 6.00s, 2.8x (Anthropic figures, Aug to Oct 2026)
  • •Claude chat and Cowork p90 3.36s to 2.17s; Claude API p50 time to first token (including model time) 1.53s to 1.01s, both about 1.5x
  • •Since CLI 2.1.300 (released Oct 14), large requests to api.anthropic.com are gzipped even with telemetry off; set CLAUDE_CODE_GZIP_REQUEST_BODIES=0 to disable
  • •A community measurement in issue #90457 found real request bodies compress about 2.1x and one machine saved 185 MB of upload in about 3 hours
  • •Gzip shrinks bytes on the wire, not billed tokens
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OpenAI / OpenAI Developers@OpenAI·17h ago
🔥 Trending

OpenAI Upgrades GPT-6 Astra Pro in ChatGPT with Parallel Multi-Agent Execution and Showcases Always-On 'dots' Triaging 3 AM Incidents via Codex

OpenAI announced a major upgrade to GPT-6 Astra Pro in ChatGPT for Pro users, introducing faster response times, stronger plugin and tool orchestration, and native parallel multi-agent execution where multiple agents tackle different parts of a complex task simultaneously. Concurrently, OpenAI Developers showcased how internal engineers put always-on 'dots' agents on call to autonomously triage 3 AM production incidents, launch Codex debugging threads, and clear codebase maintenance backlogs.

OpenAI Upgrades GPT-6 Astra Pro in ChatGPT with Parallel Multi-Agent Execution and Showcases Always-On 'dots' Triaging 3 AM Incidents via Codex
⚡ Key Takeaways
  • •Parallel multi-agent execution in GPT-6 Astra Pro: upgrades speed and tool use while enabling multiple agents to work on different task components concurrently in ChatGPT Pro
  • •Always-on 'dots' on-call incident triage: autonomously investigates 3 AM production alerts and initiates Codex coding threads before waking the engineer
  • •Mobile steering & backlog automation: engineers supervise Codex fixes from the ChatGPT mobile app and deploy Codex to continuously clear repository maintenance backlogs
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C
Cursor@cursor_ai·17h ago
🔥 Trending

Cursor Cloud Agents Can Now Build and Share Interactive Canvases for Live Dashboards and Technical Reports

Cursor (@cursor_ai) announced that Cursor Cloud Agents can now build Interactive Canvases. While a cloud agent works through a repository, developers can ask it to construct interactive dashboards or visual technical reports and share the finished canvas directly with their team via a single link.

Cursor Cloud Agents Can Now Build and Share Interactive Canvases for Live Dashboards and Technical Reports
⚡ Key Takeaways
  • •Interactive Canvases in Cursor Cloud Agents: allows developers to request live interactive dashboards and visual reports while the agent works
  • •Instant team link sharing: finished canvases can be shared with teammates as a direct URL for collaborative review
  • •Clean repository workflow: keeps diagnostic visualizations and interactive PR walkthroughs in cloud canvases without polluting git history
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