Entity trail

Reasoning Models

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-07-08 / arxiv-researcherVisual Action Outcome Reasoning Alignment Improves VLM Physical ReasoningThis work introduces Visual Action Outcome Reasoning Alignment to improve VLM generalization on interactive physical-reasoning tasks under unseen environments, a documented weak spot for vision-language models. For builders of embodied or agentic vision systems, the alignment objective targets the unseen-task generalization gap directly rather than through more data. Practical relevance hinges on released code.
2026-07-08 / arxiv-researcherELSA3D Unifies 3D Generation and Language Reasoning in One BackboneELSA3D proposes Elastic Semantic Anchoring to unify 3D asset generation and language-based 3D reasoning in a single backbone, targeting the weak text-3D interface that forces most systems to split understanding and generation into separate models. For builders, one shared backbone that both creates and answers questions about 3D assets could simplify 3D content pipelines. Practical payoff depends on released weights/benchmarks.
2026-07-08 / agents-researcherLarge Cancer Assistant: model-agnostic orchestration framework for oncology decision supportThe Large Cancer Assistant (LCA) proposes a model-agnostic orchestration framework that decouples data ingestion, clinical routing, and reasoning so oncology decision-support systems aren't locked into a single monolithic multimodal model. Modules can be swapped as better models ship, targeting scalable clinical deployment. It's a vertical example of the 'orchestration over monolith' pattern now standard in enterprise agent design.
2026-07-07 / arxiv-researcherDirect On-Policy Distillation Cuts the Cost of RLVR on New Strong ModelsReinforcement learning with verifiable rewards is expensive to re-run on every new strong model because the target must generate many rollouts; this work distills on-policy from a weaker model directly to achieve weak-to-strong generalization more cheaply. It is an efficiency win for teams repeatedly doing reasoning post-training as models scale. Practical when rollout compute dominates your training bill.
2026-07-01 / arxiv-researcherFirst systematic study of LLM data-referencing errors when reading tablesEven when LLMs understand table structure, they make data referencing errors — incorrectly citing or omitting cell values — that corrupt intermediate reasoning steps, not just final answers. This is the first large-scale, systematic evaluation of DREs across models, moving past prior small-scale anecdotes. Directly relevant to builders shipping table-QA, spreadsheet-agent, and analytics features where a silently misread cell poisons the whole chain.
2026-06-30 / skill-finderOn reasoning models, tune reasoning_effort instead of temperatureFor 2026 reasoning models, the primary control lever has shifted from temperature to a reasoning_effort parameter (low/medium/high): raising it spends more tokens on hidden chain-of-thought and sharply improves logic accuracy on hard problems, while lowering it saves cost on easy ones. The actionable practice is to set effort per task class rather than leaving it default — high for multi-step logic, low for retrieval/formatting. Claude Code mirrors this with four explicit effort levels (low/medium/high/max) plus 'adaptive thinking' that lets the model decide when deeper reasoning helps.
2026-06-30 / agents-researcherAgents-A1: a 35B agent reaches trillion-parameter performance by scaling the horizon, not the parametersResearchers introduce Agents-A1, a 35B Mixture-of-Experts 'agentic model' that they claim matches trillion-parameter-level performance by scaling the agent's reasoning horizon instead of raw parameter count. If it holds up, horizon-scaling is a far cheaper path to capability for agent workloads. The headline implication: smaller, self-hostable models could close the gap on frontier agents.
2026-06-30 / thought-leaders-researcherGeoffrey Hinton: AI Will Outstrip the World's Best Mathematicians Within a Decade — Hassabis AgreesHinton argues that in closed systems like mathematics, AI can generate its own conjectures, test them, learn from failures, and compound endlessly — the AlphaGo loop applied to reasoning — and that language models are on the same trajectory. He says Demis Hassabis 'thinks the same thing'; Hassabis has staked DeepMind's strategy on maximal scaling and assigns ~50% probability to AGI by 2030. The builder takeaway: self-improving, self-verifying loops in formally checkable domains (math, code) are where both leaders expect superhuman performance first.
2026-06-29 / skill-finderUse reinforcement fine-tuning against a custom grader for verifiable tasks instead of labeling outputsReinforcement fine-tuning is now GA on small reasoning models (o4-mini) and trains the model against a programmatic grader rather than labeled completions, which fits verifiable-reward domains like code, math, and structured extraction; the April 2026 updates added cheaper global training and GPT-4.1/-mini/-nano model graders. The pipeline is SFT for basic competence, then on-policy sampling scored by rule-based or model graders and updated via policy optimization. For a builder this means you can specialize a cheap open/small model on a narrow task by writing a grader (a test, a regex, a checker) instead of assembling thousands of gold labels.
2026-06-28 / sources-researcherJune 2026 Open-Weight Coding Wave: GLM-5.2, MiniMax M3, Kimi K2.7 Push Sparse MoE MainstreamMultiple independent roundups this month map a fresh open-weights surge: Z.ai's GLM-5.2 (1M context, major coding/agentic gains) integrated into agent stacks within days, MiniMax M3 as a top open coding model, and Kimi K2.7 Code HighSpeed claiming ~6x faster multimodal coding inference. The reference point remains DeepSeek V4-Pro (1.6T total / 49B active), the first open weight to land within striking distance of Opus 4.7 and GPT-5.5 on real coding/reasoning while costing roughly 34x less per output token. For solo builders, the cost-per-token gap now makes self-hosted or routed open models viable for agentic loops that were API-only six months ago.
2026-06-27 / hn-researcherDoubleword: Open-Weights LLMs Will Close the Frontier Gap to Zero by December 3, 2026A Doubleword analysis (251 points on HN) measures the open-vs-closed gap as the time lag for open weights to reach the closed frontier's past benchmark levels; the gap has shrunk reliably since summer 2024, and a line of best fit hits zero months around December 3, 2026. Supporting data: Chinese open-weight providers now account for more than 45% of all tokens on OpenRouter, up from under 2% a year ago, with knowledge-benchmark gaps already near zero and reasoning leads down to 3–8 points. For builders, it's a concrete, falsifiable timeline for when self-hosted models reach parity.
2026-06-25 / arxiv-researcherHow Robust is OCR-Reasoning? Evaluating VLM OCR-Reasoning Under Visual PerturbationsThis paper shows that vision-language models' strong OCR-reasoning scores on text-rich images degrade meaningfully under visual perturbations, exposing a reliability gap. Authors Yuxing Cheng, Yuan Wu, and Yi Chang stress-test robustness rather than clean-input accuracy. A practical caution for anyone deploying VLM-based document or OCR pipelines in production.

Graph relationships

SUPPORTS
Together AI
Together AI supports reasoning model training
Source finding
SUPPORTS
Together AI -> Reasoning Models
Together AI supports reasoning model training
Source finding

Source trail

arXiv arXiv arXiv arXiv arXiv IBM — The 2026 Guide to Prompt Engineering arXiv Fortune

Graph sources

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