News & Research
The latest AI research and news with real-world stakes — each item sourced, dated, summarized in plain English, and tagged by impact area. Every item is checked against its source before it appears.
Research
OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs
Anna Sofia Lippolis, Mohammad Javad Saeedizade, Stefan Schmid et al.
arXiv · 2026-07-20
OntoExtend is a framework that uses large language models (LLMs) with retrieval-augmented generation (RAG) to extend existing ontologies based on explicit requirements expressed as competency questions (CQs). Evaluated on 39 CQs across two real-world use cases—an EU-project ontology (Onto-DESIDE) and an industrial ontology from Bosch—the generated ontology fragments showed few structural issues, passed all functional evaluation tests, and were rated by ontology engineers as needing only minor to moderate revision before integration. The paper demonstrates that LLMs can serve as practical drafting assistants for requirement-driven ontology extension, reducing the resource-intensive and error-prone nature of manual ontology engineering, while noting sensitivity to CQ specificity and modeling profile.
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Research
What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification
Guosheng Li, Fenghui Ren, Bin Liu et al.
arXiv · 2026-07-20
This paper investigates how well different AI adaptation strategies for classifying corporate climate disclosures hold up when the source of the text changes—e.g., from annual reports to press releases or earnings calls. The authors test three approaches (definitions, few-shot examples, and fine-tuning) across eleven large language models and two corpora sharing the same label space but drawn from different sources. They find that strategies strongest within a single source—such as similarity-based retrieval and LoRA fine-tuning—lose much of their advantage under source shift, while simpler approaches like randomly selected few-shot examples and well-matched definitions transfer more reliably. The key takeaway is that when the text source changes, simpler adaptation strategies are often safer and more robust than more sophisticated in-source methods.
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Research
The Autonomous Agency Scale: A Behavioral Framework for Measuring Self-Directed Behavior in AI Systems
Samuel Presgraves
arXiv · 2026-07-20
The Autonomous Agency Scale (AAS) is a behavioral framework that measures self-directed behavior in AI systems across seven dimensions—cognitive autonomy, temporal persistence, environmental agency, social agency, creative agency, self-awareness, and goal formation—scored on a 0–5 scale in both active and ambient (idle) temporal bands. The framework introduces an Idle-Gap Test to distinguish genuine self-direction from scheduled rule-following by observing whether internally derived activity persists after all external triggers are removed. Applied to six contemporary systems including task agents (Claude Code, Manus, Hermes), consumer assistants (ChatGPT, Siri), and a persistent companion architecture (Airi), results show task agents score 2.3–2.4 in the Active band but only 0.6–1.9 in the Ambient band, with all idle-period behaviors traceable to user-configured schedules, while the companion architecture was the only system whose idle-period behavior survived trigger removal. The paper matters for AI policy and certification because it fills a measurement gap by quantifying autonomous agency separately from cognitive capability or catastrophic risk, though the authors note limitations including single-rater provenance and developer-evaluator bias in the longitudinal assessment.
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Research
DeLIVeR: Decomposed Learning for Information-grounded Veracity Recognition via Reinforced Knowledge Graph Exploration
Cong Hoan Nguyen, Thomas Hoang, Hieu Minh Duong et al.
arXiv · 2026-07-20
DeLIVeR is a framework for automated fact-checking that addresses 'query brittleness' in traditional retrieval systems by decomposing complex claims into targeted question sets and exploring structured Knowledge Graphs for evidence. A Planner LLM is optimized using Group Relative Policy Optimization (GRPO), rewarding structural diversity and verdict accuracy. Evaluated on the LIAR, FEVER, and PolitiFact benchmarks with Qwen2.5-7B, the framework achieves peak F1-scores of 83.73, 84.57, and 79.70 respectively, representing a 10–15% improvement over HippoRAG2. The approach provides an auditable, transparent path for verifiable misinformation detection, with implications for content quality assurance and policy around AI-driven fact-checking.
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Research
HALLMARK: Diagnosing Three Failure Modes in LLM Citation Verifiers
Patrik Reizinger, Wieland Brendel
arXiv · 2026-07-20
HALLMARK is a new benchmark of 2,526 BibTeX entries designed to evaluate and diagnose how well LLM-based and rule-based citation verifiers detect hallucinated references in academic writing. The benchmark spans 14 hallucination types, three difficulty tiers, and six diagnostic sub-tests per entry, and is used to evaluate DOI-lookup baselines, frontier LLMs, tool-augmented agents, and a co-designed rule-based verifier. The study finds that false-positive rate—not recall—is the primary deployment bottleneck, with an order-of-magnitude spread in FPRs across systems determining whether a verifier's flags are mostly true catches or noise; additionally, most LLMs over-flag papers published after their training cutoff. The work is motivated by real-world stakes, noting that GPTZero found 53 papers with hallucinated citations among NeurIPS 2025 accepted submissions, underscoring the growing risk to the scientific record.
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Research
PEARL: Auditable Repair for Scientific Reasoning Graph Extraction
Bohan Su, Pengze Li, Yuchen Lu et al.
arXiv · 2026-07-20
PEARL is a training-free framework that converts noisy large language model outputs into structured, auditable scientific reasoning graphs by repairing malformed syntax, misaligned edge labels, and weak source anchors under a strict Peircean schema. On the ARCHE benchmark—five archives of 70 papers each—PEARL raises strict gate passes from 0 out of 350 (LLM baseline) to 300 out of 350, while average REA improves from 0.339 to 0.906. The framework preserves a full audit trail during repair, making it suitable for research-agent and AI scientist workflows that require inspectable, semantically valid reasoning traces rather than unconstrained graph regeneration. This matters for quality assurance in AI-assisted science, where verifiable links between evidence and conclusions are critical.
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Research
Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field
Tanay Aggarwal, Angelo Salatino, Francesco Osborne et al.
arXiv · 2026-07-20
This paper evaluates five small, open-source large language models (up to 9 billion parameters) on their ability to identify semantic relationships between biomedical concepts, a key step in automated ontology generation. The authors introduce MeSH-Rel-4K, a new dataset of 4,000 semantic relationships drawn from Medical Subject Headings (MeSH), and benchmark three adaptation strategies: standard prompting, Chain-of-Thought prompting, and fine-tuning. Results show that targeted fine-tuning increases the average F1-score by 34.1 percentage points over baseline approaches, overcoming reasoning limitations typical of smaller models. This work matters for knowledge management and quality assurance in biomedical research, demonstrating a scalable, automated pathway for constructing and maintaining specialized ontologies without requiring large-scale compute resources.
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Research
A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification
Bogdan Raduta, Horia Velicu, Alexandru Preda et al.
arXiv · 2026-07-20
SIFT (Self-Improving, Frozen-gate Training) is a document classification system designed to address the practical enterprise barriers of labeling costs and safe autonomous retraining. It uses a cheap CPU-bound pipeline (SPLADE sparse encoder with a LightGBM head) that escalates only low-confidence cases to an LLM judge, whose verdicts are written back into a growing labeled corpus so the cheaper model continuously improves from production traffic rather than upfront annotation. Safety is enforced by a two-part promotion gate combining a critical-label F1 regression check and a frozen golden test set that the model never trains on, preventing silent accuracy regressions. The paper argues this architecture makes autonomous monthly retraining practical for enterprises and trends the marginal labeling cost toward zero.
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Research
Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI
Bogdan Raduta, Horia Velicu, Alexandru Preda et al.
arXiv (Cornell University) · 2026-07-20
This paper presents HALO (Hallucination-Aware Layered Oversight), a six-layer assurance architecture designed to make enterprise AI systems reliably trustworthy by treating hallucination as a containable system failure rather than an eliminable model property. The architecture combines grounded generation over approved content, constrained execution, multi-signal verification using both LLM judges and evidence-based source checks, calibrated abstention when grounding is insufficient, full traceability, and continuous drift monitoring. The authors argue that waiting for a hallucination-free model is the wrong strategy, and instead propose that 'zero hallucination' is a property a system enforces rather than a property a model possesses. This matters for enterprises deploying AI in regulated contexts—such as claims extraction—where undetected fabricated outputs carry significant operational and compliance risk.
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Research
When a Name Is Not a Name: A Benchmark Dataset and Distilled Reasoning for Culturally Entangled Bangla Homographs in Low-Resource LLMs
Md. Asaduzzaman Shuvo
arXiv · 2026-07-20
This paper introduces a benchmark dataset of 1,516 expert-verified Bangla sentences designed to test whether language models can correctly disambiguate words that function as both personal names and culturally loaded common nouns—a problem termed Culturally Entangled Homograph (CEH) disambiguation. The authors find that current open- and closed-source models exhibit a systematic 'dominant-meaning bias,' defaulting to the common-noun reading and missing the name sense, and that Bangla-specific pretraining alone does not resolve this cultural gap. They show that contrastive chain-of-thought prompting and knowledge distillation of cultural explanations into small (1–3B parameter) models can reduce dominant-meaning bias from as high as 100% to under 5%, turning a previously failing Bangla-specific model into the strongest system tested. These findings matter for quality assurance and enterprise NLP deployments in low-resource language settings, where cultural misreadings in automated text processing can lead to systematic errors.
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Research
Financial Audit Assistance using Misinformation Detection and Explanation
Kshitij Madhav Jadhav, Sushodhan Vaishampayan, Manoj Apte et al.
arXiv (Cornell University) · 2026-07-20
This paper presents an AI-assisted system for financial auditing that uses unsupervised techniques to automatically detect misinformation in financial statements (Balance Sheet, Income Statement, and Cash-flow Statement) and generate explanations identifying likely sources of that misinformation. The system leverages a large corpus of 11,460 financial statements over five years along with associated audit reports to produce data-driven insights that support auditors in validating accuracy, integrity, and the absence of material misstatement. By automating detection and explanation, the system aims to reduce the time, complexity, and expertise burden currently required of human auditors. This has direct implications for enterprise risk analysis, corporate governance, and the broader quality assurance of financial reporting processes.
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Research
Reasoning as a Double-Edged Sword: Architecture and Cross-Stage Robustness in Vision-Language-Action Models
Tuan Duong Trinh, Naveed Akhtar, Basim Azam
arXiv · 2026-07-20
This paper investigates whether adding a reasoning step to Vision-Language-Action (VLA) models improves their robustness to adversarial or stochastic perturbations. Testing three architectures—no reasoning, text chain-of-thought, and latent iterative loop—across vision, reasoning, and action stages on LIBERO and SimplerEnv benchmarks, the authors find that the latent-iterative model is the least robust, with task success collapsing under both noise and white-box attack, while its fragility is structural rather than depth-dependent. The study also shows that runtime safety monitors based on reasoning outputs fail under adaptive attacks, with a plan-action consistency probe falling to chance and action-anomaly probe fusion never exceeding undefended performance. These findings matter for quality assurance and certification of robotic AI systems, establishing a performance ceiling that any viable defense must surpass.
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Research
Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring
Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu et al.
arXiv · 2026-07-20
This paper addresses the challenge of explaining deep learning predictions in business process monitoring, where neural networks forecast the outcomes of ongoing operational cases but are difficult to interpret. The authors propose a local post-hoc explainability method that uses a control-flow-aware segmentation algorithm to partition process traces into meaningful segments, then computes SHAP explanations at the segment level rather than at the raw event or aggregated trace level. This approach is validated on a synthetic dataset with known process logic and on real-world event logs from a loan application process and a Dutch municipality's administrative process. The work matters for enterprise and quality-assurance contexts because it improves trust and practical adoption of AI-driven process monitoring by helping users identify which parts of a case drive a predicted outcome.
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Research
Decode-Time Grammars: Constrained LLM Generation over a Refinement Order of Grammar Fragments
Shuoming Zhang, Ruiyuan Xu, Haofeng Li et al.
arXiv · 2026-07-20
This paper introduces 'decode-time grammars,' a technique for constraining large language model (LLM) code generation so that generated code references only symbols, fields, APIs, or options that actually exist in the current runtime environment. Unlike standard grammar-constrained decoding, which ensures syntactic correctness but still allows references to undefined names, decode-time grammars dynamically instantiate grammar fragments from the runtime environment and update those constraints as new declarations are generated, provably eliminating 'ghost references' to undefined symbols. The approach is implemented in a system called gproj and evaluated across TileLang, SQL, and P4 with models ranging from 0.6B to 236B parameters, achieving this semantic correctness guarantee at moderate overhead. This matters for enterprise and quality-assurance contexts where LLM-generated code is compiled or executed automatically without human review, particularly for domain-specific languages and custom APIs where model reliability is currently brittle.
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Research
ETAS: An Effect-Typed Language for Agent Systems
Huiri Tan, Yikun Wang, Puyang Zhang et al.
arXiv · 2026-07-20
ETAS is a new programming language designed specifically for AI agent systems that treats agents, tool calls, prompts, memory, human approvals, and policies as formal language-level constructs rather than informal library conventions. Its type system statically tracks both the effects an agent computation may produce and the sequence of actions it may request, enabling compile-time policy checks and runtime authorization enforcement. The language formalizes key correctness properties—including type/effect soundness, policy safety, and handler trace-transparency—and is implemented in Rust with diagnostics and trace-aware execution hooks. This work matters for enterprise and policy contexts because it provides a principled foundation for reasoning about authorization, auditability, and nondeterminism in AI agent systems before and during execution.
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Research
Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models
Dongdong Yang, Deyue Zhang, Zhao Liu et al.
arXiv · 2026-07-20
This paper introduces MIND, a cognitive jailbreak framework that targets Text-to-Image (T2I) generative models by reframing adversarial prompt generation as a belief-state inference problem over latent defense mechanisms. Unlike prior approaches that treat model feedback as a binary success/failure signal, MIND uses multi-modal feedback (textual refusal, visual blocking, semantic sanitization) to iteratively profile defenses and retrieve historically effective attack strategies. Tested on the I2P benchmark against Stable Diffusion v1.5 under six defense settings, MIND achieves an Attack Success Rate of 95.62%, and reaches 91.58% ASR on the commercial Wan-2.5 system. The findings highlight significant remaining vulnerabilities in both open-source and commercial T2I systems to adaptive, semantically consistent adversarial attacks.
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Research
FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches
Jiacheng Ding, Cong Guo, Jason Xu
arXiv · 2026-07-20
This paper introduces WC2026-Agents, a contamination-free benchmark in which four frontier LLMs (Claude Opus 4.8, GPT-5.5, Gemini 3.1 Pro, and Grok) acted as autonomous forecasting agents across all 104 matches of the 2026 FIFA World Cup, each occurring after the models' training cutoffs. Each model gathered web evidence, committed to a win/draw/win probability distribution, placed a virtual $100 bet, and reflected after seeing the final score, with pre-match bookmaker odds serving as an economically grounded baseline. Key findings show the four agents agreed on the same top pick 92% of the time and none beat the market's Brier score, while a naive flat-stake strategy on the market favorite outperformed all four agents; yet the agents diverged sharply as decision-makers, with betting ROI ranging from -18% to +10% and self-reported error rates on wrong picks ranging from 36% to 86%. The benchmark reveals that frontier models can differ substantially on calibration, decision quality, and self-knowledge even when their raw predictions are nearly identical, providing a replicable framework for evaluating LLM forecasting and reasoning in enterprise and quality-assurance contexts.
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Research
WuYu-EnvLE-Bench: A Benchmark for Evaluating Large Language Models in Environmental Law Enforcement
Ziliang Yang, Yi Zhang, Kaijun Lin et al.
arXiv · 2026-07-20
WuYu-EnvLE-Bench is a new benchmark for evaluating large language models (LLMs) in environmental law enforcement, built from real enforcement cases, regulatory standards, and expert review. It includes 2,521 instances spanning 14 tasks and 12 pollution-medium subdomains across pre-, in-, and post-enforcement workflows, using two scoring metrics (AES and IEI) to assess capability, response quality, and resource efficiency. Results show LLMs perform well on rule-bounded tasks but struggle with evidence-chain construction, contradiction detection, multi-source integration, and procedural judgment. Notably, scaling up model size shows diminishing returns, with medium-sized models nearly matching larger ones on structured tasks, highlighting the need for evidence-grounded and task-adaptive reasoning before LLMs can be reliably used in enforcement contexts.
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Research
SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategy Refinement in E-Commerce Recommendation
Hanchen Yang, Kaiwen Yang, Junpeng Zhuang et al.
arXiv · 2026-07-20
SR-Agent is an agentic LLM-based framework designed to automate the refinement of post-ranking strategies in industrial e-commerce recommender systems, addressing the problem that statically configured strategies degrade user experience as the online environment evolves. The system combines three components—a UserSim agent for surfacing bad user experience cases, an Analysis agent for diagnosing recurring issues, and a Strategy Refinement Harness that maps diagnoses to bounded corrective actions with a four-stage reward pipeline and reversible rollback. Deployed on the Kuaishou e-commerce platform, a one-month online A/B test showed increases of 0.71% in order volume, 0.34% in browsing depth, and 0.48% in clicked-category diversity, while shortening the refinement cycle and lowering operational cost. This work demonstrates that agentic AI can replace slow, costly manual processes in large-scale recommendation systems, with measurable business and user experience gains.
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Research
C$^2$KV: Compressed and Composable KV Cache Reuse for Efficient LLM Inference
Chuheng Du, Junyi Chen, Hanlin Tang et al.
arXiv · 2026-07-20
C²KV introduces a unified framework for reusing key-value (KV) caches in large language model inference, targeting the storage and access bottlenecks that arise in long-context applications like retrieval-augmented generation and multi-document reasoning. The system learns a composable, compressed KV cache representation that is position-agnostic, using a lightweight sidecar Extractor with learnable compression tokens and a structured attention flow, without modifying the frozen base model. A compression-concatenation co-training strategy aligns extracted representations with their downstream reuse behavior, enabling flexible concatenation across contexts. Experiments across multiple long-context benchmarks and model families show C²KV achieves up to 17× inference speedup while preserving generation quality, making it relevant to enterprise deployments of LLMs at scale.
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Research
Integrating High-Level Requirements to Low-Level Tests with Machine-Readable V&V Specifications
Mansur Arief, Nur Ahmad Khatim, Ali Akarma et al.
arXiv (Cornell University) · 2026-07-20
This paper introduces VNVSpec, an open-source framework that makes verification and validation (V&V) specifications machine-readable and executable, bridging the gap between high-level system requirements and the low-level automated tests that software teams actually run. The framework allows users to state or import requirements from standards-derived catalogs, decompose them into module-level requirements with explicit metrics and acceptance criteria, and link these to test results through a traceability graph that produces audit-ready reports. The authors evaluate VNVSpec by self-application, running it in continuous integration against its own specification of 36 requirements verified by 449 tests, with execution time that scales linearly and can handle up to 10,000 requirements. This matters especially for AI-enabled and cyber-physical systems, where regulators increasingly demand traceable evidence that high-level requirements are met—evidence that raw test results alone cannot provide.
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Research
Informal Learning Emerges in Everyday Human-LLM Interaction
Zixin Chen, Haotian Li, Ziang Xiao et al.
arXiv · 2026-07-20
This study analyzes 128,569 naturalistic human-LLM conversations to determine whether everyday AI use fosters informal learning or merely offloads cognition to the model. The researchers translated learning-science constructs into measurable behavioral signatures, finding that cognitive engagement appeared in 31.9% of 491,685 user turns and constructive engagement—the deepest learning-oriented form—appeared in 4.9%, indicating that deeper sense-making is recurrent but selective. Scaffolded assistant support was consistently associated with richer constructive participation, with effects varying by user framing, task type, support form, timing, and prior user state. The findings suggest that AI evaluation should shift beyond answer-delivery efficiency toward preserving users' cognitive opportunities to reason and construct understanding.
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Research
Verify, Repair, Repeat, or Stop? Robust Stopping for Noisy Verify-Repair Loops in LLM Agents
Yitao Wu, Si Shen, Rui Yang et al.
arXiv · 2026-07-20
This paper introduces VRR-Stop, a principled stopping framework for verify-repair loops used in LLM agents for tasks like code generation and mathematical reasoning. The core problem is that when both the verifier and repairer are noisy, continued repair can corrupt already-correct solutions, causing reported acceptance to rise while true validity declines. VRR-Stop uses a four-parameter noise model and belief filtering to estimate when further repair is genuinely beneficial, paired with a fallback mechanism (VRR-Guard) for low-discrimination settings. On a GSM8K stress test, VRR-Stop improves final true validity by 60.6 percentage points over a fixed five-round repair baseline at an average cost of only 0.72 repair rounds.
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Research
Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models
David Rannaleet, Victor Gunnarsson, Bo Bernhardsson et al.
arXiv · 2026-07-20
This paper investigates the use of diffusion probabilistic models (DPMs) to generate synthetic speech-evoked EEG data for Auditory Attention Decoding (AAD), a technique used in hearing aids to track a listener's focus in real time. Because real-world EEG data is scarce, training deep learning models for short time windows (≤1 second) is particularly challenging. The authors show that augmenting training datasets with DPM-generated synthetic EEG data significantly improves AAD classification performance compared to models trained on measured data alone (p<0.05). These findings suggest that diffusion-based data augmentation can help overcome data scarcity limitations and improve the robustness of hearing aid AI systems.
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Research
Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion
Yongchan Hong, Defu Cao, Wenjin Liu et al.
arXiv · 2026-07-20
This paper introduces RELIABLE-BA, an evidential framework that fuses predictions from multiple protein-ligand docking engines while providing interpretable uncertainty estimates grounded in molecular context. Rather than treating all engine outputs equally, the method models each engine as an evidential expert using Normal-Inverse-Gamma distributions, scales uncertainty based on learned reliability, and aggregates predictions in closed form. On the PDBBind and BDB2020+ benchmarks, as well as SARS-CoV-2 Mpro and 5HT2A receptor datasets, the approach achieves competitive accuracy while substantially improving uncertainty calibration, and filtering to high-confidence predictions reduces error by up to 25%. This matters for drug discovery pipelines where knowing which AI predictions to trust can directly improve the quality and efficiency of candidate selection.
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