Reasoning

HIT Shenzhen, Huawei, and Shenzhen Hetao College proposed Dynamic-dLLM, a training-free dLLM acceleration framework that raises LLaDA-8B-Instruct throughput on GSM8k from 8.32 TPS to 37.29 TPS with almost no accuracy loss.

OpenAI launched GPT-Realtime-2, GPT-Realtime-Translate, and GPT-Realtime-Whisper as API models, covering 128K-context voice reasoning, streaming translation from more than 70 input languages into 13 output languages, and realtime transcription priced at $0.017 per minute.

Ai2 researcher Nathan Lambert visited Zhipu, Moonshot AI, Tsinghua, Meituan, Xiaomi, and 01.AI within 36 hours, and said Chinese labs closely watch ByteDance and respect DeepSeek, while student participation in core work, open source habits, and in-house control of the technical stack mark key differences.

Doug_Bitterbot says TOPAS scored 11.67% on ARC-AGI-2 using one RTX 4090 after about 14 days of training. The 100M-parameter checkpoint hit 36% locally, but recursive TTT caused null outputs on nearly half of Kaggle puzzles. The key detail is time management: the author expects 20% after threshold tuning and 3-5 more weeks of training.

inclusionAI says Ling-2.6-1T is now live on OpenRouter. The trillion-parameter instruction model uses “fast thinking” and claims top AIME26 and SWE-bench Verified results with about 75% lower cost. The post does not disclose pricing, context length, or full benchmark scores.

Qwen released WebWorld 32B/14B/8B, Qwen3 finetunes for training and evaluating web agents. It uses 1M+ real web trajectories and supports 30+ step simulation plus A11y Tree, HTML, XML, Markdown, and natural-language states. Agents trained on its synthetic trajectories gain 9.9% on MiniWob++ and 10.9% on WebArena.

OpenAI introduced new realtime voice models in its API for voice intelligence. The RSS snippet says they reason, translate, and transcribe speech; the post does not disclose counts, pricing, or limits.

DeepSeek could reach a $45B valuation in its first investment round, according to the title. The snippet says it rose in early 2025 after training an LLM with far less compute and cost; the post does not disclose round size, investors, or terms.

A Reddit user ran Qwen3.6 27B NVFP4 on one RTX 5090 32GB and validated 200k context in vLLM. The setup used fp8_e4m3 KV cache, FlashInfer, and MTP with 3 speculative tokens; a 10-run 200k pass completed with 73.6 tok/s mean generation and 70.2s TTFT. The key constraint is 32GB VRAM: logs showed 8.3GiB KV cache and about 30478MiB total GPU use.

OpenAI made GPT-5.5 Instant the default ChatGPT model, rolling it out free to all users. AIME 2025 rose from 65.4% to 81.2%, responses are 30.2% shorter, and hallucinations fell 52.5% versus GPT-5.3 Instant on high-risk prompts. Plus and Pro web users get chat, file, and Gmail personalization first; the API model ID is chat-latest.

The article examines how AI use can replace independent judgment with after-the-fact review, citing Shaw and Nave. It says review shifts toward familiarity checks; the post does not disclose experiment numbers.

Alex Lupsasca says GPT-5 reproduced his paper result in 11 minutes after a textbook warmup prompt, and ChatGPT later generated 110 pages of graviton calculations in one day; the team spent three weeks verifying the results before writing a quantum-gravity paper.

OpenAI says ChatGPT’s default GPT-5.5 Instant reduced hallucinations in internal evaluations. Versus GPT-5.3 Instant, hallucinated claims fell 52.5% on high-stakes prompts. Inaccurate claims fell 37.3% on flagged hard chats; the post does not disclose full eval size.

OpenAI released GPT-5.5 Instant as ChatGPT’s new default model. The company says it reduces hallucinations in law, medicine, and finance while keeping prior low latency; the post does not disclose benchmarks, rollout scope, or pricing.

Hugging Face’s post-training team published an interactive guide comparing RL environment frameworks. The team spent one month building environments in verifiers, OpenEnv, Nemo-Gym, OpenRewards, and others, then trained models to study scaling. The post does not disclose benchmark scores, model sizes, or training costs.

OpenAI updated ChatGPT’s default model to GPT-5.5 Instant for default chat use. The RSS snippet says answers are more accurate, hallucinations are reduced, and personalization controls improved; the post does not disclose metrics, pricing, or context window.

Agent-World builds 1,978 environments and 19,822 tools to train agents on long-horizon tasks. It combines web mining, tool generation, verifiable task synthesis, and GRPO training, with tasks averaging over 15 turns. The key signal is the scaling link among environment count, self-evolution rounds, and 23 benchmarks.

PolyU and Sichuan University introduced SignThought, accepted to ACL 2026 Main and slated for oral recommendation. It uses latent thoughts, plan-then-ground, and dual-stream decoding, reaching top gloss-free BLEU-4 on five SLT benchmarks. The team also built LC-HKSLT with 1,311 hours, 432K clips, and 14 signers.

Xinzhiyuan says Google DeepMind used Aletheia on 700 Erdős problems and got 13 original answers. The pipeline had Gemini Deep Think produce 200 candidates, then a verifier reduced them to 63. The post says Erdős-75 had a wrong premise, yet Aletheia wrote dozens of proof pages.

The title says Anthropic reviewed Claude Code regressions involving three bugs. It names reasoning-strength changes, a cache optimization error, and a system-prompt length limit; the post does not disclose repro steps, timeline, or fix status. The key point is AI reviewing AI code under engineering constraints.