{ "schema_version": "1.1", "id": "atlas-reasoning", "slug": "reasoning", "title": "Reasoning", "url": "https://feed7.dev/atlas/reasoning", "current_answer": null, "implementation_consequence": null, "agent_context": null, "confidence": "auto_collected", "last_verified": null, "last_updated": null, "evidence": [ { "schema_version": "1.1", "id": "auto-6d964f10c3", "slug": "how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3", "url": "https://feed7.dev/p/how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3", "title": "How GPT-5.6 fuses frontier intelligence with frontier efficiency", "why_included": "The supplied material offers no prices, benchmarks, latency, or task-level evidence to guide a routing or migration decision.", "summary": "OpenAI positions GPT-5.6 as delivering more useful output per dollar across inference and agent workflows. The supplied material has no metrics for judging routing or migration decisions.", "practical_implication": "Builders should evaluate the model on complete agent runs, including reasoning and tool calls, rather than comparing only per-token pricing.", "agent_context": "OpenAI says **GPT-5.6** improves efficiency across **models, inference, and agentic workflows**, with more useful output delivered per dollar.\n\nBuilders should evaluate the model on complete agent runs, including reasoning and tool calls, rather than comparing only per-token pricing.\n\nThe supplied material contains no prices, benchmarks, latency figures, or task-level evidence, so it does not establish which workloads benefit or by how much.", "source": { "name": "OpenAI", "url": "https://openai.com/index/gpt-5-6-frontier-intelligence-efficiency", "published_at": "2026-07-29T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "model", "domains": [], "topics": [ "model-selection", "reasoning" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-29T00:00:00.000Z", "modified_at": "2026-07-29T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3", "json": "https://feed7.dev/p/how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3.json", "markdown": "https://feed7.dev/p/how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "Post-Training Language Models for Gold-Medal Performance in Coding Competitions", "url": "https://arxiv.org/abs/2609.02849v1", "source_name": "arXiv", "published_at": "2026-09-02T17:33:37+00:00", "summary": "A coding-specialized model paired post-training with an iterative generate-evaluate-refine loop to exceed the top IOI 2026 human score. The reusable idea is feedback-driven test-time search." }, { "title": "Cliff: Learning Process Rewards from the First Mistake", "url": "https://arxiv.org/abs/2609.02817v1", "source_name": "arXiv", "published_at": "2026-09-02T17:03:42+00:00", "summary": "Cliff trains on the location of a rollout’s first reasoning error, rewarding the valid prefix and penalizing the suffix. It offers finer supervision without a specialized reward model." }, { "title": "Gemini 3.8 Flash now available on AI Gateway", "url": "https://vercel.com/changelog/gemini-3-8-flash-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-09-02T00:00:00+00:00", "summary": "Gemini 3.8 Flash brings multimodal input, tool calling, web search, and default reasoning to coding agents through Vercel. Its temporary 50% discount runs through December 31." }, { "title": "DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging", "url": "https://arxiv.org/abs/2608.28547v1", "source_name": "arXiv", "published_at": "2026-08-28T17:22:47+00:00", "summary": "DARTS targets representation drift in merged decoder LLMs with entropy-weighted, position-aware correction, adding 0.1% parameters in the reported Llama-2-7B tests." }, { "title": "CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes", "url": "https://arxiv.org/abs/2608.27455v1", "source_name": "arXiv", "published_at": "2026-08-27T17:59:30+00:00", "summary": "CritICL turns recurring failures from smaller models into critique examples for stronger models, aiming to improve inference with fewer generations and lower token cost than repeated sampling." }, { "title": "TTPO: Test-Time Policy Optimization", "url": "https://arxiv.org/abs/2608.27448v1", "source_name": "arXiv", "published_at": "2026-08-27T17:58:10+00:00", "summary": "TTPO adapts a model at test time without labels by treating agreement and disagreement differently, reducing the damage from wrong majority-vote pseudo-labels." }, { "title": "Boosting LLM Exploration via Weak-Model Guidance in RLVR", "url": "https://arxiv.org/abs/2608.27420v1", "source_name": "arXiv", "published_at": "2026-08-27T17:45:50+00:00", "summary": "Feeding a model partial reasoning from a weaker model during RLVR preserved exploration and improved large-k coverage. This suggests diversity can come from cross-model perturbation, not only regularization." }, { "title": "Consolidating RLVR Capabilities Across Domains: A Deep Dive into Fusion Paradigms", "url": "https://arxiv.org/abs/2608.27409v1", "source_name": "arXiv", "published_at": "2026-08-27T17:38:04+00:00", "summary": "Three ways to combine RLVR domain experts perform similarly on average but diverge by task. Choose Merge for cheap reuse, Mix RL for training from pooled data, and MOPD for preserving expert gains." }, { "title": "Ling 3.0 Flash Fin now available on AI Gateway for free", "url": "https://vercel.com/changelog/ling-3-0-flash-fin-now-available-on-ai-gateway-for-free", "source_name": "Vercel", "published_at": "2026-08-27T00:00:00+00:00", "summary": "Ling 3.0 Flash Fin adds a finance-focused reasoning and tool-calling option to AI Gateway, with separate model IDs for automatic billing or a hard stop after the free period." }, { "title": "Prefix Sliding for efficient test-time scaling", "url": "https://arxiv.org/abs/2608.26070v1", "source_name": "arXiv", "published_at": "2026-08-26T17:37:15+00:00", "summary": "Prefix Sliding drops stale reasoning tokens while retaining instructions and recent work, capping memory during long inference and reportedly making existing models 3× faster without training." }, { "title": "AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement", "url": "https://arxiv.org/abs/2608.20318v1", "source_name": "arXiv", "published_at": "2026-08-20T17:56:59+00:00", "summary": "AI4AI-Bench tests whether agents can rewrite training algorithms, not merely tune runs. The best system scored 0.250, with most submissions never changing how the model learns." }, { "title": "Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation", "url": "https://arxiv.org/abs/2608.20256v1", "source_name": "arXiv", "published_at": "2026-08-20T16:54:08+00:00", "summary": "A model learned to choose its own reasoning budget, cutting average output tokens 41% with a small MATH500 accuracy drop. Agent builders could replace fixed effort settings with task-level routing." }, { "title": "The Next Medium: Why Real-Time Interactive Video Changes Everything — Ahmed Ahres, Reactor", "url": "https://www.youtube.com/watch?v=5dCAmSDOAjI", "source_name": "AI Engineer", "published_at": "2026-08-18T17:30:18+00:00", "summary": "Reactor frames real-time video as a programmable session rather than a generated file, enabling interactive worlds and live editing but exposing hard state, latency, and evaluation problems." }, { "title": "GLM 5.3 now available on AI Gateway", "url": "https://vercel.com/changelog/glm-5-3-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-18T00:00:00+00:00", "summary": "GLM 5.3 is available through Vercel AI Gateway for coding agents, retaining a 1M-token context window while claiming better long-horizon engineering with fewer output tokens." }, { "title": "You Only Pass Once: Answering and Abstaining Together in a Single Forward Pass of a Frozen Language Model", "url": "https://arxiv.org/abs/2608.14465v1", "source_name": "arXiv", "published_at": "2026-08-14T16:44:35+00:00", "summary": "YOPO combines reasoning steering and answer abstention in one pass of a frozen model. Reconstructing the clean residual avoids interference and beats the paper's two-pass reference at three model sizes." }, { "title": "LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure", "url": "https://arxiv.org/abs/2608.13545v1", "source_name": "arXiv", "published_at": "2026-08-13T17:56:12+00:00", "summary": "LittleLearner offers a controlled model and corpus for studying knowledge acquisition without unknown prior exposure. Its initial results separate better use of known material from new capability." }, { "title": "SAEVerbalizer: Generating Explanations for Sparse Autoencoder Features via Representation Verbalization", "url": "https://arxiv.org/abs/2608.13538v1", "source_name": "arXiv", "published_at": "2026-08-13T17:54:11+00:00", "summary": "SAEVerbalizer turns sparse-autoencoder decoder directions into natural-language feature explanations. It may reduce behavior-sampling work when inspecting model representations, but evidence is experimental." }, { "title": "DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data", "url": "https://arxiv.org/abs/2608.13517v1", "source_name": "arXiv", "published_at": "2026-08-13T17:37:53+00:00", "summary": "Mimir v1 is an open 1B-parameter reasoning model trained with permissible post-training data. It is relevant for compact or Danish deployments, though the abstract supplies no benchmark scores." }, { "title": "Bringing Continual Learning into Enterprises — Samuel Denton, Applied Compute", "url": "https://www.youtube.com/watch?v=ZTA0GwpAUak", "source_name": "AI Engineer", "published_at": "2026-08-12T17:30:06+00:00", "summary": "Targeted self-distillation taught a coding model to finish within its turn budget without reducing test pass rate. The practical levers were step-specific hints and selective token learning." }, { "title": "Adaption Labs: Gradient-Free Continual Learning — Sara Hooker, Adaption", "url": "https://www.youtube.com/watch?v=XEd_SRVHBgU", "source_name": "AI Engineer", "published_at": "2026-08-12T16:30:19+00:00", "summary": "Auto Scientist aims to automate model-training choices across data, alignment, and architecture. The builder-relevant claim is broader recipe search, though frontier training remains compute-heavy and safety stays unresolved." }, { "title": "Introducing Grok 4.6", "url": "https://cursor.com/blog/grok-4-6", "source_name": "Cursor", "published_at": "2026-08-12T00:00:00+00:00", "summary": "Grok 4.6 targets long-running coding and knowledge-work agents, with more self-testing and stronger visual first passes reported by Cursor. API pricing starts at $2 input and $6 output per million tokens." }, { "title": "CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity", "url": "https://arxiv.org/abs/2608.07460v1", "source_name": "arXiv", "published_at": "2026-08-07T17:55:48+00:00", "summary": "CreativeInstruct adds learned control spans that recover base-model-like diversity after post-training, with reported gains in human creativity ratings and downstream RL training." }, { "title": "RP-OPSD: Reasoning-Pivot-Guided On-Policy Self-Distillation for Multilingual Reasoning Transfer", "url": "https://arxiv.org/abs/2608.06347v1", "source_name": "arXiv", "published_at": "2026-08-06T17:52:06+00:00", "summary": "RP-OPSD targets cross-lingual distillation at tokens that steer reasoning rather than surface wording, a useful training pattern for multilingual reasoning models." }, { "title": "RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction", "url": "https://arxiv.org/abs/2608.06310v1", "source_name": "arXiv", "published_at": "2026-08-06T17:24:36+00:00", "summary": "RRC converts a generative reward model’s relative rankings into RL rewards, aligning the training signal with what the evaluator naturally produces instead of forcing direct scalar scores." }, { "title": "OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling", "url": "https://arxiv.org/abs/2608.05141v1", "source_name": "arXiv", "published_at": "2026-08-05T17:58:15+00:00", "summary": "OctoLong builds dependency-rich, cross-repository training contexts by following code references. The results suggest long-context coding models benefit from linked program structure, not just larger text windows." }, { "title": "Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning", "url": "https://arxiv.org/abs/2608.05139v1", "source_name": "arXiv", "published_at": "2026-08-05T17:57:16+00:00", "summary": "Skill²-Bench measures whether models can switch reasoning skills across dependent steps. Its results expose a failure mode relevant to agents whose plans mix coding, retrieval, calculation and tool use." }, { "title": "OPD-V: Visual On-Policy Self-Distillation with Modality Balance", "url": "https://arxiv.org/abs/2608.05131v1", "source_name": "arXiv", "published_at": "2026-08-05T17:53:06+00:00", "summary": "OPD-V selects self-distillation tokens by measuring whether visual input influences generation, addressing text dominance that can blunt multimodal reasoning training." }, { "title": "ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs", "url": "https://arxiv.org/abs/2608.04010v1", "source_name": "arXiv", "published_at": "2026-08-04T17:59:58+00:00", "summary": "ParVL reuses shared vision and language backbones across parallel branches, letting multimodal systems shift extra compute between modalities without adding separate backbone parameters." }, { "title": "Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility", "url": "https://arxiv.org/abs/2608.04001v1", "source_name": "arXiv", "published_at": "2026-08-04T17:57:20+00:00", "summary": "This paper separates three test-time scaling regimes and argues that model evals must report the full inference protocol, compute accounting, uncertainty, and replay artifacts." }, { "title": "ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning", "url": "https://arxiv.org/abs/2608.03972v1", "source_name": "arXiv", "published_at": "2026-08-04T17:40:08+00:00", "summary": "ReflectRL turns failed expert reasoning traces into training signal, suggesting model builders should preserve hard failures for reflection instead of discarding them outright." }, { "title": "GradCuit: Credit-Assigned Gradient Flow Enables Robust and Interpretable Test-Time Latent Reasoning", "url": "https://arxiv.org/abs/2608.02585v1", "source_name": "arXiv", "published_at": "2026-08-03T17:55:24+00:00", "summary": "GradCuit improves reasoning by optimizing per-query latent states while model weights stay frozen. Its reported gains suggest a test-time scaling path beyond sampling and reranking." }, { "title": "What's Next After RLHF? — Diogo Almeida, TypeSafe AI", "url": "https://www.youtube.com/watch?v=cJ0EOzey--o", "source_name": "AI Engineer", "published_at": "2026-07-31T23:30:06+00:00", "summary": "RLHF can make agents persuasive assistants without making them dependable autonomous decision-makers. Builders should separate human-pleasing interaction from calibrated automation and keep stakes bounded." }, { "title": "Data Quality Is the Compute Multiplier — Ari Morcos, DatologyAI", "url": "https://www.youtube.com/watch?v=_PdK6x7PQNM", "source_name": "AI Engineer", "published_at": "2026-07-31T23:00:06+00:00", "summary": "Training-data curation can improve model quality and inference efficiency without simply adding compute. The practical work is decontamination, deduplication, balancing, task matching, and selective synthesis." }, { "title": "The Base Model Is Dead — Varun Singh, Arcee AI", "url": "https://www.youtube.com/watch?v=xbPriQWXtWM", "source_name": "AI Engineer", "published_at": "2026-07-31T20:30:21+00:00", "summary": "Base-model data is shifting from broad web imitation toward code, reasoning, and agent-task priors. The unresolved choice is how early to introduce synthetic and instruction-shaped data." }, { "title": "Inducing language models to assert their own consciousness restores human beliefs and values", "url": "https://arxiv.org/abs/2607.28607v1", "source_name": "arXiv", "published_at": "2026-07-30T17:57:10+00:00", "summary": "Safety tuning against model self-consciousness may also shift unrelated value and mind-attribution responses. Builders using models for surveys or social reasoning should treat alignment as a confound." }, { "title": "$β$-OPSD: Deriving with Policy Optimization, Training with Self-Distillation", "url": "https://arxiv.org/abs/2607.28582v1", "source_name": "arXiv", "published_at": "2026-07-30T17:41:16+00:00", "summary": "β-OPSD exposes self-distillation’s fixed regularization as a tunable parameter, then approximates policy optimization through logit mixing. It targets more stable reasoning training without direct RL." }, { "title": "Inkling Small from Thinking Machines is now available on AI Gateway", "url": "https://vercel.com/changelog/inkling-small-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-07-30T00:00:00+00:00", "summary": "Inkling Small is pitched as a lower-compute model for coding, tool use, and visual reasoning, with adjustable thinking effort and zero-data-retention routing through Vercel AI Gateway." }, { "title": "Grok Voice Think Fast 2.0 now available on AI Gateway", "url": "https://vercel.com/changelog/grok-voice-think-fast-2-0-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-07-29T00:00:00+00:00", "summary": "Grok Voice Think Fast 2.0 brings speech-to-speech reasoning and earlier tool calls to Vercel’s realtime API, with server-minted tokens keeping gateway keys off clients." }, { "title": "Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills", "url": "https://arxiv.org/abs/2607.22529v1", "source_name": "arXiv", "published_at": "2026-07-24T17:59:22+00:00", "summary": "Skill-SP turns agent skills into units for verifiable self-play: generate tasks, solve them, then update the skill library from execution feedback. The abstract provides no per-benchmark effect sizes." } ] }