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Competence-Gated Pooling of Language Models and Priors for Event Forecasting

Paper proposes a competence gate pooling language model forecasts with external priors, improving Brier score from 0.0771 to 0.0732 across 2,357 binary questions.

The paper defines a language model's relative competence as its marginal value beyond an available external forecast, and derives conditions under Brier loss where model disagreement improves that forecast. A competence gate estimates domain-level source weights from resolved outcomes, shrinks uncertain estimates toward a global weight, and recalibrates the pooled forecast. Across 2,357 resolved binary questions and five language models, it improves the external baseline from 0.0771 to 0.0732 Brier and beats global forecast combinations, though it defers to the market on ForecastBench. Across four Qwen models, verbal confidence failed to identify when the model outperformed the external forecast, while outcome-estimated competence supported better abstention.

Hugging Face daily papersSep 9, 20:00 UTC · 6d agoAI research

AI for Military Support

Study of 2,015 Israeli military personnel found algorithmic aversion toward AI targeting decision support, reduced when explainable AI features were added.

The paper 'Black Box Warfare' reconstructed a real-world military AI decision-support system used in targeting and tested a high-fidelity replica in two experiments with 2,015 Israeli military personnel. Contrary to automation-bias fears, participants showed strong algorithmic aversion, especially in high-collateral-damage scenarios. Integrating explainable AI features reduced aversion and promoted more thoughtful evaluation of algorithmic recommendations. The authors conclude that trust in military AI is dynamic and that human agency remains central in high-stakes decisions.

Schneier on SecurityAug 11, 11:18 UTC · Aug 11, 2026AI research

Thought without systematicity? Evaluating reasoning models on rule induction tasks

Study finds reasoning models often fail on structurally equivalent variants of tasks they solve, suggesting their reasoning lacks systematicity.

The paper extends rule induction tasks from cognitive science using task isomorphisms such as recombination and substitution to test systematicity in reasoning models. Despite solving tasks correctly, models frequently fail on structurally equivalent variants of the same task. The authors conclude many model behaviors lack systematicity, making it difficult to establish cognitive abilities beyond the specific evaluation contexts.

Hugging Face daily papersSep 11, 20:00 UTC · 4d agoAI research

Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

A distillation framework compresses LLM reasoning into a 15.5M-parameter trade-up recommendation model reaching AUC 0.941 with product-type test-time training.

The paper targets trade-up recommendation, which identifies higher-quality alternatives that preserve customer purchase intent. A retrieval-augmented few-shot LLM teacher generates labels and rationales that supervise a compact embedding-pair classifier; at inference the 15.5M-parameter student uses only two precomputed 768-dimensional embeddings with no LLM calls. On 8,352 annotated pairs, label-only training scored AUC 0.912, reasoning distillation reached 0.924, and product-type test-time training lifted it to 0.941 with average precision 0.940. The distilled student is roughly 5,000x faster and 10,000x cheaper than direct LLM inference on a 100K-pair proxy catalog.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:08 UTC · 11d agoAI research

When LLM judges agree, should we believe them?

Amazon ICML paper uses Ising models to correct correlated LLM-judge votes, beating accuracy-weighted panels by 9-14%.

Amazon Science describes an ICML paper, "Dependence-aware label aggregation for LLM-as-a-judge via Ising models," addressing how correlated judge outputs inflate majority-vote confidence. The unsupervised method models pairwise dependence between judges, learning both reliability and similarity without human reference labels. Tested on relevance, toxicity, and summarization tasks with 10 judge models at temperature zero, it outperformed accuracy-weighted voting by 9% to 14%.

Hacker News · AISep 14, 16:29 UTC · 1d agoAI research HN 41↑ · 23 comments

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:57 UTC · 5d agoAI research

An Evidence Model for Agentic Processes: Evidence Claims, Trust Assumptions, and Policy Assessment

Researchers propose an evidence claim model defining which trust and audit claims agentic AI systems can support, mapping claims to mechanisms, assumptions, and threats.

The paper proposes an evidence claim model for agentic AI processes that exchange messages, invoke tools, request approvals, and modify shared artifacts. It distinguishes claim types such as artifact integrity, provenance, approval evidence, and policy assessment, mapping each to mechanisms, assumptions, limitations, and threats. It stresses that hashes, signatures, and external anchors do not establish semantic truth, authorization, or capture completeness. The contribution is conceptual, offering vocabulary for what an agentic black box can and cannot evidence and which controls must surround it.

arXiv cs.CRSep 8, 09:25 UTC · 8d agoAI safety & security

The Frontier AEO Tracker: What Astra Chooses (and every other frontier model, and what you can do about it)

Latent Space launches an AEO tracker scoring 7 frontier models' product recommendations across 161 categories, revealing generational bias flips.

Latent Space built a tracker measuring Answer Engine Optimization by running 6 prompt variations across 7 frontier models with search enabled over 161 product categories, scoring first choices, alternatives, mentions, and anti-recommendations. It found 28 categories with a universally dominant primary choice and observed soft biases, such as models favoring their own lab's coding agents. Analysis of Anthropic's Sol→Astra and Opus→Fable generations showed newer models consulting fewer sources and being less likely to change answers when questions are paraphrased.

Latent SpaceSep 7, 21:32 UTC · 8d agoAI research

One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

A review paper frames on-policy self-distillation collapse as governed by three levers: token weighting, privileged information, and guidance decay.

The paper critically reviews On-Policy Self-Distillation (OPSD), where a language model trains on its own generations scored token-by-token by a teacher conditioned on privileged information such as reference solutions or environment feedback. It identifies collapse, the progressive narrowing of producible reasoning paths, as the dominant failure mode and analyzes it through three levers: signal weighting, the nature of privileged information, and teacher dynamics. The review is restricted to mathematical reasoning, reports no new experiments, and offers a shared vocabulary separating settled findings from disputed ones.

Hugging Face daily papersAug 25, 20:00 UTC · 21d agoAI research

Causal Foundation Models

A paper introduces causal foundation models (CFMs): pretrained networks that estimate treatment effects on new datasets via in-context learning without fine-tuning.

Causal foundation models (CFMs) apply the foundation-model paradigm to causal inference, replacing bespoke per-problem estimator pipelines with networks pretrained once at scale. CFMs estimate causal quantities such as the average treatment effect on entirely new datasets through in-context learning, without model updates. The work serves as a practical introduction to the emerging area, covering background in causal inference and machine learning and including example code and Jupyter notebooks.

Hugging Face daily papersSep 1, 20:00 UTC · 14d agoAI research

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.

K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.

arXiv cs.AI / cs.LG / cs.CLSep 14, 16:49 UTC · 1d agoAI safety & security

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

New framework tests whether LLM-cited explanation factors are necessary or sufficient, finding weak correlation across Claude, GPT, and Gemini models.

An arXiv paper introduces black-box intervention tests measuring whether factors LLMs cite in their explanations are necessary or sufficient for their outputs in agent oversight workflows. Across eight models from the Claude, GPT, and Gemini families, Spearman correlations between cited rankings and measured influence ranged from 0.349-0.354 (advisor recommendation) to 0.431-0.580 (prompt monitoring). Uncited factors scored above the lowest cited factor in up to 57.6% of advisor responses, showing cited top-three factors do not reliably identify the most influential inputs.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:37 UTC · 11d agoAI safety & security1

AI models' written reasoning steps correspond to distinct internal patterns, a new study finds

KAIST and Naver AI Lab researchers show LLM reasoning steps like extraction and computation map to distinct activation patterns, strongest in middle layers.

Researchers at KAIST and Naver AI Lab defined eight recurring reasoning operations, including extraction, decomposition, formula recall, deduction, and computation, and showed they correspond to separable activation patterns in Qwen2.5-7B, Qwen3-8B, and Gemma4-31B on math tasks, with GPT-5 labeling solution segments. The separation peaks in middle layers, holds even when a computation step produces a wrong answer, and goes beyond surface-level token choice. Findings replicated on Llama-3-8B, and classifiers trained on Qwen3-8B transferred to GPQA-Diamond and MATH-500. The authors note that using internal states for error detection or mid-generation steering remains future work.

The DecoderSep 12, 13:39 UTC · 3d agoAI research2

Learning-Guided Planning in Large Dynamic Action Spaces: Budgeted Tree Search for One-to-Many Mobile Charging

LP-BTS uses graph proposal policies, learned critics, and budgeted PUCT search to plan mobile charging across dynamic action spaces up to 2,813 stops.

LP-BTS is a learning-guided planning architecture for one-to-many mobile charging, where N=250 sensors induce roughly 1,125 initial candidate charging stops. A graph proposal policy concentrates candidate support, a learned value critic evaluates leaves, and edge-budgeted PUCT compares simulated futures, letting a single frozen checkpoint cover action universes from 736 to 2,813 stops. On a sealed 30-scenario confirmatory bank it attains the highest observed survival (0.4545) and alive-AUC (0.8031), though its +0.0066 survival edge over the strongest engineered comparator is statistically unresolved.

arXiv cs.AI / cs.LG / cs.CLSep 15, 16:42 UTC · 18h agoAI research

Toward an Empirical Probabilistic Risk Manifestation Model of Organizational Cybersecurity in SMEs

Empirical study of 22 SME security assessments builds a probabilistic risk model and shows assessments can be cut 24-45% while retaining most critical findings.

Researchers analyzed 281 validated security findings from 22 real-world SME cybersecurity assessments conducted over two years via a pro bono university clinic. They derived an empirical Risk Manifestation Model linking eight organizational security functions to two exposure conditions, five attack mechanisms, and six outcome categories, using probability propagation to identify dominant risk pathways. The dominant pathway runs from asset exposure to credential compromise to unauthorized access, stable under leave-one-organization-out analysis. Retaining six functions reduces assessment burden by 24% while preserving 97% of critical findings; five functions cut burden 45% while preserving 89% of critical findings.

arXiv cs.CRSep 14, 01:15 UTC · 2d agoResearch

Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization

ModerationBench shows foundation models can nearly triple Bluesky's moderation F1 (0.60 vs 0.22), with instruction- and example-driven guidance performing comparably.

Researchers built ModerationBench, a new benchmark of 4,000 manually annotated in-the-wild posts from Bluesky, to test whether foundation models can reliably operationalize content moderation policies. They systematically compare instruction-driven guidance (reasoning from policy precepts) with example-driven guidance (generalizing from precedents) for Vision-Language Models. Both paradigms achieve comparable peak effectiveness, and foundation models nearly triple the F1 of Bluesky's deployed moderation system on Random Posts (0.60 vs 0.22).

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:30 UTC · 6d agoAI research1

Why judgment is emerging as cybersecurity’s defining skill

CyberScoop op-ed argues CISOs should grant AI autonomy based on reversibility and blast radius rather than model confidence, and measure analyst overrides of AI recommendations.

A CyberScoop op-ed contends that as AI takes over analysis and recommendations in security operations, human judgment about context, reversibility and blast radius becomes the defining skill. The author argues autonomy decisions should rest on how reversible and impactful an action is rather than model confidence, citing examples such as patching vendor-certified medical devices and a service account whose 3 a.m. login spikes were normal quarterly-close activity. It also urges leaders to measure analyst approvals, edits and rejections of AI recommendations, and review latency, instead of automation rates or mean time to resolution.

CyberScoopSep 4, 10:00 UTC · 12d agoIndustry

PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization

PLC-DPO corrects noisy preference labels during DPO by routing each pair as clean, flip, or tie using calibrated policy-reference margins.

PLC-DPO addresses reversed, weak, or ambiguous preference labels in Direct Preference Optimization by using the calibrated policy-reference margin as online evidence to route each pair's training signal as clean, flip, or tie. This actively corrects supervision direction and strength rather than only filtering suspicious examples. Across 57 dataset-model-benchmark cells, PLC-DPO achieves the best mean win rate of 60.5 versus 55.5 for the next-best method. Injected-noise and tie stress tests plus human disagreement analysis show the routing stays stable and distinguishes flipped from weakly directional pairs.

Hugging Face daily papersAug 30, 20:00 UTC · 16d agoAI research

AI for Games in the Foundation Model Era

Survey organizes foundation-model AI for games into six roles and analyzes which capabilities transfer across playing, design, building, runtime adaptation, and testing.

A survey maps foundation-model and learned world-model research across the game lifecycle into six roles: playing/acting, modeling players and games, designing games, building/maintaining games, runtime generation/adaptation, and testing/evaluation. The authors identify cross-role connections such as trajectories training world models and design specifications driving executable implementations. Control schemes, rules, engine interfaces, state representations, and player contexts often remain setting-specific, so downstream claims require validation in the target setting. Evaluation is most standardized for bounded game playing, while persistent state, repeated revision, validated player modeling, and automated testing remain less established.

Hugging Face daily papersSep 14, 20:00 UTC · 1d agoAI research

Diffusion Models and Concept Formation

Paper argues diffusion models implicitly form Cobweb-like concept hierarchies, with a basic level emerging at intermediate noise levels.

The authors draw a formal correspondence between diffusion models and Cobweb, a classic incremental concept-hierarchy learner, noting both are hierarchical Bayesian density models with Gaussian prototypes. Modes of the diffusion model's noisy marginals form a hierarchy whose basic level sits at intermediate noise, where class identity commits. The correspondence is tested on MNIST and Fashion-MNIST via mode-finding. Diffusion is reframed as a cognitive model of concept formation.

arXiv cs.AI / cs.LG / cs.CLSep 11, 16:43 UTC · 4d agoAI research

Large Language Models Develop Belief State Geometry In-Context

Probing six open-source LLMs on HMM-generated data shows belief states are linearly decodable (R² 0.83–0.99), suggesting in-context learning approximates Bayesian prediction.

Researchers prompted six open-source LLMs with data from 40 hidden Markov models selected for non-trivial belief structure and probed residual-stream activations for belief states (posteriors over hidden states). Belief states were linearly decodable with peak R² values of 0.83–0.99 across HMM/LLM combinations, spanning early to late layers. Patching and steering the probe-identified subspace preserved downstream prediction quality while control interventions degraded performance substantially, establishing functional relevance. The results provide representation-level evidence that in-context learning approximates optimal Bayesian prediction over a context-inferred generative model.

arXiv cs.AI / cs.LG / cs.CLSep 15, 16:17 UTC · 18h agoAI research

From Intent to Execution Grant: An Execution-Boundary Conformance Profile for High-Risk AI Actions

Researchers specify EBL-Core, an execution-boundary conformance profile binding AI agent intents, policies, and evidence into verifiable execution grants, validated with bounded tests.

The paper defines EBL-Core, a conformance profile deciding whether one fully materialized AI-generated candidate action may receive action-scoped execution authority. It binds a structured intent object, Root and Operational Policies, typed evidence, and a verifiable Decision Derivation through an Execution Release Contract, with lifecycle rules for Redemption and Revocation. Evaluation included 34 static vectors, 15 lifecycle checks, and 100 trials of 32 concurrent Redemption attempts yielding exactly one winner per trial. The authors state these bounded results demonstrate executability of the specified subset, not production readiness or complete mediation.

arXiv cs.CRSep 10, 14:21 UTC · 5d agoAI safety & security1

ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.

ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.

Hugging Face daily papersSep 10, 20:00 UTC · 5d agoModel release

Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models

Audits of 10 classifiers on BRFSS show target leakage, not model class, drives the reported 0.89 AUROC in survey-based cardiovascular screening.

The study benchmarks ten model classes, including glass-box and tabular foundation models, for prevalent myocardial infarction on 442,067 respondents of the 2022 BRFSS across five feature tiers of decreasing leakage risk. Removing two post-diagnostic features costs every model 0.049-0.051 AUROC and collapses performance into a 0.0045-wide band, and the explainable boosting machine matches all alternatives within 0.005 while scoring roughly 104x faster than the strongest foundation model. Frozen models transport within 0.002 AUROC to 2023 data; the authors conclude evaluation practice and feature sets, not model capacity, are the binding constraint.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:29 UTC · 5d agoAI research1

Discrete Beckmann Transport Models for One-Step Language Modeling and Reasoning

DBTM achieves one-step text generation via a time-independent transport map trained directly from data, removing pretrained teacher distillation.

Discrete Beckmann Transport Models (DBTM) build a time-independent flow whose autonomous transport map provably carries any point in ambient space to a fixed point on simplex vertices in a single step. The fixed-point property is characterized by a conservation equation whose residual can be minimized directly from data, eliminating the need for a teacher flow, distillation, and time conditioning. A partial-context interpolant extension turns additional function evaluations into refinement steps rather than ODE integration steps. On language modeling and reasoning tasks, DBTM's one- and few-step generation improves quality and accuracy over discrete diffusion and continuous flow baselines.

arXiv cs.AI / cs.LG / cs.CLSep 14, 17:19 UTC · 1d agoAI research

Before You Poll with LLMs: A Deliberative Diagnostic Framework

Deliberative diagnostic shows all five tested frontier LLMs misrepresent human belief shifts after arguments, with GPT-5.1 reversing on outgroup questions.

The Deliberative Polling Diagnostic Framework compares human and LLM persona belief shifts after identical informational interventions, using data from America in One Room (526 personas, 72 questions). All five frontier models tested failed uniquely: GPT-5.1 exhibited partisan reversal (80% on outgroup vs 26% on policy questions), Gemini 2.0 Flash, Claude Sonnet 4.5 and Llama 3.3 70B overshot at 5-7x human magnitude, and DeepSeek V3 showed near-zero change (rigidity). The authors term the underlying signature 'self-sycophancy', conformity to the model's internal persona stereotype rather than reasoning from provided information.

arXiv cs.AI / cs.LG / cs.CLSep 14, 16:41 UTC · 1d agoAI research

MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes

MetroLLM-Bench is a 955-case benchmark testing language models as transit kiosk tool-calling runtimes across six real metro systems.

The benchmark covers 37-414-station metro systems and eleven task categories including routing, fare calculation, disruptions, accessibility, and adversarial input, with 14 deterministic and 8 semantic scoring components. Of 26 models from six vendors, a PEFT-tuned 4B Qwen 3.5 student scored 91.3 on Tier 1, exceeding GPT-5.6 (90.6/90.0), while Muse Glimmer 30B led the composite ranking. A deterministic rule-based baseline reached 84.6, and PEFT gains over base models shrank from +7.03 points at 2B to -0.91 at 27B.

Hugging Face daily papersSep 8, 20:00 UTC · 7d agoAI research

MasterControl Seventeen Every Time

Governed enterprise analytics study shows deterministic policy execution matched 110/110 answer-and-evidence contracts while runtime agent planning matched none.

The paper studies a governed approach where a language model interprets the question while deterministic policy selects and runs a pre-approved analytical program returning results and evidence. Across 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B only interpreted intent and policy executed the approved program. None of 330 runtime-planning episodes satisfied the full answer-and-evidence contract, whereas the policy-executed analyzer matched 110 of 110. The authors note this is configuration-specific and expressiveness is preserved via relational operations, aggregation, comparison, windows, ranking, and similarity with replayable results.

Hugging Face daily papersSep 1, 20:00 UTC · 14d agoAI research

Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization

Researchers release OR-Clarify, a benchmark testing whether LLM agents ask clarifying questions before formulating optimization models from incomplete requests.

OR-Clarify evaluates pre-formulation clarification in operations research: each task gives a partial problem description, withholds structured hidden slots, and scores agents via bounded interaction with a simulated user, measuring slot recovery, stopping behavior, silent assumptions, and interaction cost. The authors also propose InterOPT, a two-stage framework that identifies formulation-critical gaps to decide when to ask or stop. In choice-based experiments InterOPT substantially outperforms all baselines in exact slot recovery and remains competitive in the open-ended setting.

Hugging Face daily papersSep 3, 20:00 UTC · 12d agoAI research1

Discovery Foundation Models: Toward Open-Ended Discovery Intelligence

Proposes Discovery Foundation Models that participate in creating new problems and knowledge, instantiated in Zetema and the GALILEO therapeutic-discovery system.

The paper formulates Discovery Foundation Models (DFMs) as general-purpose systems for open-ended discovery, supporting seven coupled capabilities from problem discovery through evidence-grounded revision and continual improvement. It instantiates the framework with Zetema, combining explicit research-state dynamics, verification gating, external grounding, and cross-task Discovery Skill evolution. GALILEO, a real therapeutic-discovery system, closes the loop between Dry-Lab reasoning, robotic and hands-on Wet-Lab experimentation, and iterative hypothesis revision. The authors also define process-centered evaluation so discovery behavior can be trained and measured beyond final answers.

arXiv cs.AI / cs.LG / cs.CLSep 14, 17:56 UTC · 1d agoAI research 2 sources