Search: “IntelliJ IDEA”

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.CL·Sep 14, 17:56 UTC · 2d agoAI research 2 sources

Cognition helps Devin test its own work with GPT‑6 Astra

Cognition integrates GPT-6 Astra into Devin, its CLI, and desktop products to automate testing and provide evidence for code review.

OpenAI published a customer case study on Cognition, maker of the autonomous software engineer Devin, which serves customers from large banks to startups. Cognition uses GPT-6 Astra across its cloud agent, CLI, and desktop products; in one example, Devin uses Astra to test the iPhone game Otter Run and returns a simulator recording plus a report of passed checks and untested areas. Co-founder Walden Yan says Astra's ability to test and prove work functions as expected could reduce manual code review. The team also uses Astra to fix customer-reported bugs from screenshots and return verification screenshots faster.

OpenAI News·updated Sep 12, 20:00 UTC · 4d ago·first Sep 11, 16:00 UTC · 5d agoAI industry 11 sources1

JetBrains security advisory (AV26-891)

Canada's Cyber Centre relayed a JetBrains advisory covering vulnerabilities in GoLand, Hub, IntelliJ IDEA and YouTrack.

Advisory AV26-891, dated September 8, 2026, notes JetBrains vulnerabilities affecting GoLand prior to 2026.2.2.1, Hub prior to 2026.2.52442, IntelliJ IDEA prior to 2026.2.2, and multiple YouTrack versions. Users and administrators are encouraged to review JetBrains' advisory and apply any necessary updates. No specific CVEs or exploitation details are provided in the relayed advisory.

Canadian Centre for Cyber Security·Sep 8, 17:27 UTC · 8d agoAdvisory

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark with 660 instances measures whether LLMs can spot and fix underspecified research-method details for faithful implementation.

Researchers introduce IdeaAMBIG, a benchmark of 660 evidence-grounded instances (163 real-world gaps from reproducibility reports and GitHub issues, 497 controlled synthetic gaps) built from papers, codebases, and reproduction artifacts. It evaluates codification-readiness assessment, defect localization, and clarification action generation. Across 13 LLMs, the best model achieved only 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% clarification success when given the annotated defect. An oracle study showed gold resolutions raise the codification-ready rate from 14% to 98%, identifying defect localization as the main bottleneck.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:59 UTC · 7d agoAI research2

QuickFox Supply Chain Attack Delivers FDMTP Backdoor via Trojanized Windows Installer

The Hacker News·Aug 5, 05:49 UTC · Aug 5, 2026Malware

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark of 660 specification-gap instances shows LLMs localize implementation-critical research gaps poorly, with best model at 9.6% defect recovery.

IdeaAMBIG is a benchmark of 660 evidence-grounded instances evaluating whether research-method specifications provide enough information for faithful implementation: 163 real-world gaps from reproducibility reports and GitHub issues plus 497 controlled synthetic gaps. It tests codification-readiness assessment, defect localization, and clarification action generation across 13 LLMs. The best model achieves only a 9.6% Macro Defect Recovery Rate on real-world instances, though 80.6% clarification success when given the annotated defect, and an oracle study shows gold resolutions raise codification-ready rates from 14% to 98%. Defect localization emerges as the main bottleneck across all evaluated models.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research

Former OpenAI researcher builds an AI model that judges options instead of writing text

TypeSafe AI launches Jev, a judgment-only model built by ex-OpenAI staff that classifies inputs with 70-500 ms latency instead of generating text.

Startup TypeSafe AI, co-founded by former OpenAI researcher and InstructGPT co-author Diogo Almeida, introduced Jev, a model that scores developer-defined answer options with probabilities rather than generating free-form text. The company claims 70-500 ms responses, parallel multi-question evaluation, and $0.042 per million input tokens with free outputs, targeting request routing, sales intent scoring, and assistant guardrail checks. Benchmarks are self-built and not independently verified, the 'no hallucination' guarantee only covers output structure, and access is currently via waitlist.

The Decoder·Sep 16, 15:19 UTC · 14h agoAI industry

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 papers·Sep 11, 20:00 UTC · 5d agoAI research

Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning

Tiny Aya L2-Thinker, a 3.35B model, achieves over 93 percent in-language reasoning across 60 languages via optimized multilingual data mixing; weights released.

The paper studies L2 reasoning, the ability to reason consistently in the language of the user's prompt, approached through SFT data composition and scheduling. Tiny Aya L2-Thinker (3.35B) achieves an in-language reasoning rate above 93 percent across 60 languages on six benchmarks spanning math, commonsense, instruction following, open-ended generation, and cultural reasoning. Findings show generalization to held-out languages comes from broader language coverage, multilingual non-reasoning data, and a strong English reasoning backbone, suggesting reasoning is language-agnostic and transferable without per-language supervision. Model weights and multilingual reasoning data are publicly released.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:56 UTC · 7d agoAI research1

Affora: A Design System for Agent-Friendly Interfaces

Affora is a design system making interfaces legible to computer-use agents while preserving human workflows, with reusable components and executable checks.

Affora supports both human users and computer-use agents through a shared interface rather than a separate agent-only surface. Three controlled studies cover component implementations, visual variation, and interaction-design principles, producing guidance from individual components to complete sites with reusable implementations and executable checks. Evaluation on independently authored interfaces shows gains where agent-readability deficits exist, limited effects where they do not, and a workflow case gives preliminary evidence of reduced interaction cost.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:46 UTC · 12h agoAI research

Polimill builds Japan's next-generation public AI infrastructure

OpenAI says Japanese company Polimill uses GPT models and Codex to give municipalities searchable access to administrative knowledge.

OpenAI published a customer story describing how Polimill, a Japanese civic technology company, builds public AI infrastructure using OpenAI GPT models and Codex. The tools let municipalities search and reuse administrative knowledge and are intended to accelerate internal development workflows.

OpenAI News·Aug 31, 07:00 UTC · 16d agoAI industry

Enabling Creative Exploration for Vibe Design Agents

Separating design-direction exploration from code generation via structured specifications broadens UI alternatives without destabilizing output.

The paper proposes an inference architecture for vibe design agents that makes design direction an explicit intermediate decision: a Verbalized Sampling-inspired pre-pass proposes structured design specifications with typicality scores, an external selector samples one, and a downstream generator realizes it under fixed settings. Across 168 prompts with 1,255 paired comparisons per temperature, theme sampling broadens selection coverage and screenshot variation, with LLM-judge preferences varying across interventions and prompt complexity. An online experiment with over 300,000 tasks found the code-export increase statistically uncertain, though negative feedback events decreased alongside modest operational costs.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoAI research

AIJon: Automated Generation of Annotations for Fuzzing

AIJON uses LLMs to auto-generate IJON-style fuzzing annotations, matching human-expert quality on the Magma benchmark.

Researchers replicate IJON experiments and propose AIJON, a system that uses LLMs to automatically generate expert-style annotations for coverage-guided fuzzing at scale. On the Magma benchmark, LLM-generated annotations performed comparably to human-generated ones, though annotation-based fuzzing was not strictly better than AFL++. The paper analyzes how annotations affect fuzzer energy distribution across campaigns.

arXiv cs.CR·Sep 16, 10:50 UTC · 19h agoResearch

Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models

Researchers release Explainability Assistant, an open-source conversational XAI tool using LLM function calling, lifting intent-parsing accuracy from 76.8% to 94%.

The paper introduces the Explainability Assistant, an open-source conversational XAI system for interpreting energy consumption forecasting models such as genetic-programming symbolic regressors. It uses LLM function calling instead of rigid custom grammars, achieving 94% intent-parsing accuracy versus 76.8% for prior work TalkToModel, and adapts to different ML problem types without task-specific fine-tuning. Comparative evaluation with energy domain specialists against a traditional XAI dashboard showed improved usability, with all experts preferring the conversational interface.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:40 UTC · 6d agoAI research1

How RingCentral builds AI-native work from engineering to ops

OpenAI's case study describes RingCentral using ChatGPT Work and Codex to accelerate AI product development and centralize operational intelligence across engineering and operations.

OpenAI published a customer story on RingCentral's adoption of ChatGPT Work and Codex. RingCentral reportedly uses the tools to speed up AI product development and centralize operational intelligence across engineering and operations. The post is promotional rather than technical and describes no security incidents.

OpenAI News·Aug 12, 00:00 UTC · Aug 12, 2026AI industry

Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning

Researchers train Tiny Aya L2-Thinker, a 3.35B model achieving over 93% in-language reasoning across 60 languages via multilingual data mixing.

The paper addresses L2 reasoning, where models reason consistently in the language of the user's prompt rather than defaulting to English. Through data-centric SFT optimization, the 3.35B Tiny Aya L2-Thinker reaches above 93% L2 reasoning rate across 60 languages on 6 benchmarks covering math, commonsense, instruction following, open-ended generation, and cultural reasoning. The authors find that generalization to held-out languages relies on broad language coverage, multilingual non-reasoning data, and a strong English reasoning backbone, without needing reasoning supervision in every target language. Model weights and multilingual reasoning data are released.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research1

The DeepMind Institute

Google DeepMind launches the DeepMind Institute, publishing essays on AGI reasoning transparency, economic policy for AGI, and dynamic frontier AI capability testing.

Google DeepMind introduced the DeepMind Institute, an interdisciplinary initiative on the implications of approaching AGI, with contributors including Demis Hassabis, Shane Legg, James Manyika, Rohin Shah, and Anca Dragan. Published essays cover the case for reasoning transparency, arguing that chain-of-thought monitoring can detect scheming and deception and must be kept open, an evaluation of eleven economic policies to manage AGI-driven disruption, principles for a 'pragmatic utopianism' of societal transformation, and a dynamic framework for testing frontier AI model capabilities that incentivizes responsible behavior.

Hacker News · security·updated Sep 16, 17:00 UTC · 13h ago·first Sep 16, 14:32 UTC · 15h agoAI safety & security 2 sourcesHN 27↑ · 4 comments

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:31 UTC · 7d agoAI research

From assistance to execution: How enterprises put AI to work

OpenAI reports enterprise adoption patterns of agentic AI through ChatGPT and Codex, with frontier firms pulling ahead in deployment.

OpenAI published a report on how enterprises are moving from AI assistance to autonomous execution, focusing on agentic AI deployments. It highlights the use of ChatGPT and Codex in enterprise workflows and argues that frontier firms are pulling ahead of peers in AI adoption. The piece is adoption research and marketing rather than a security disclosure.

OpenAI News·Aug 12, 06:00 UTC · Aug 12, 2026AI industry1

Beneath the Surface of Chains-of-Thought: A Mechanistic Interpretation of Reasoning Operations in LLMs

Study shows LLM reasoning operations like planning and deduction are geometrically separable in hidden states, with separability peaking in middle layers.

Researchers investigate whether functional reasoning operations — problem formulation, goal decomposition, deduction — have corresponding geometric structure in LLM hidden representations. They find operations are separable in held-out representations with separability peaking in middle layers, ruling out lexical and positional confounds; token-wise operation alignment becomes more distributed across layers, and identical surface tokens are represented differently depending on their surrounding chunk. Attention-masking interventions show chunk-onset operation-aligned representations depend on preceding reasoning context; code is released on GitHub (naver-ai/beneath-cot).

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research1

MOONWALK: Mediating Operations with Intent-Evidence-Action Alignment Across Junior-Supervisor Review Workflows in Animation/VFX Pre-Production

MOONWALK introduces an intent-evidence-action alignment workflow for animation/VFX reviews where AI handles administrative coordination while artists keep creative authority.

MOONWALK is a pre-production review system that articulates creative intent into a shared project record, anchors review judgments to grounded evidence, and converts authorized decisions into concrete revision tasks. AI components handle administrative coordination such as flagging missing context and organizing notes, not creative direction. An in-studio study against a chat-only AI interface showed stronger intent alignment, decision traceability, and checklist executability.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:10 UTC · 7d agoAI research

The full stack behind abundant intelligence

OpenAI CFO Sarah Friar describes how compounding gains across chips, compute, models, and products cut intelligence costs.

OpenAI CFO Sarah Friar explains the company's 'full stack' strategy, arguing that advances across chips, compute, models, and products compound to deliver more useful intelligence at greater scale and lower cost. The post is a corporate strategy narrative rather than a technical or product release. It frames vertical integration as the driver of OpenAI's economics.

OpenAI News·Aug 25, 07:05 UTC · 22d agoAI industry

New policy ideas for the Intelligence Age

OpenAI is funding 14 independent projects exploring AI policy ideas on economic opportunity and societal resilience.

OpenAI announced funding for 14 independent projects that explore new AI policy ideas. The funded work focuses on expanding economic opportunity and strengthening societal resilience. This is a private funding initiative rather than new regulation.

OpenAI News·Aug 17, 03:15 UTC · Aug 17, 2026AI industry

Studying Without a Syllabus: Task-Agnostic Environment Preprocessing

Paper formalizes task-agnostic environment preprocessing, where agents study unfamiliar environments under a budget to build reusable artifacts for a frozen solver.

The paper formalizes task-agnostic environment preprocessing, where a studying system explores an environment under a budget and produces artifacts like indices, scripts, or procedural guidance for a frozen solver, without task examples or evaluation feedback. The authors compare unaided and archive-equipped meta-agents against fixed synthetic-practice and corpus-processing methods across six heterogeneous benchmarks. A meta-agent variant achieves the highest Avg@3 reward on five benchmarks, while fixed corpus processing remains best on the largest corpus benchmark. Studied artifacts reduce the test-time sampling needed to reach a given score, shifting computation from repeated test-time attempts to a pre-task study phase.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research

A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM

A*-Thought-V2 compresses chain-of-thought into latent tokens using geometric hidden-state dynamics, cutting computation while improving accuracy on Qwen models.

A*-Thought-V2 models chain-of-thought as a hidden-state trajectory and interleaves explicit text with continuous latent tokens, compressing steps whose transitions deviate from the question-to-solution direction. Trained via stepwise embedding forcing and label forcing with soft multi-modal supervision, it was evaluated on Qwen3.5-9B and Qwen3.6-27B across six benchmarks. Reported results include up to 2.6% average accuracy gain, up to 50% shorter responses, 2.29x higher Accuracy per Computation Unit, 94.6% faster preprocessing, and up to 80.3% faster training.

Hugging Face daily papers·Sep 7, 13:56 UTC · 9d agoAI research

Towards Tackling Application Logic Flaws through Autonomous Formal-Logic Modeling and Automated Reasoning

LL-Verifier combines LLMs with logic model checking to automatically discover logic flaws, uncovering vulnerabilities in 27 IoT access-control protocols.

Researchers present LL-Verifier, a framework that uses LLMs to autonomously convert natural-language protocol descriptions and security goals into formal logic models in a new logic language built on Maude, then applies logic model checking for exhaustive verification. The framework targets application-logic flaws that are tied to business semantics and hard to scale with manual analysis. Evaluation on 27 access-control protocols of widely used IoT devices uncovered a range of sophisticated logic vulnerabilities with security and privacy implications.

arXiv cs.CR·Sep 9, 17:58 UTC · 7d agoResearch1

Cognitive Extensions for Dual-Process Language Agents: Memory and Self-Reflection in Interactive Environments

Researchers extend the SwiftSage dual-process agent with adaptive memory and self-reflection modules, improving scores in interactive environments.

The work adds an Adaptive Memory Module (AMM) for salience-gated episodic storage and trigger-driven retrieval, and a Self-Reflection Module (SRM) for bounded execution-time validation and corrective intervention, to the SwiftSage agent. Controlled ablations on ScienceWorld across four configurations show the full system achieves the best mean final score (64.62), success rate (43.17%), and successful-step efficiency (19.33 steps). SRM is the strongest standalone contributor, suggesting execution-time control is the dominant bottleneck while episodic memory helps once the runtime loop is stable.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:50 UTC · 12h agoAI research

Wazuh and AI For Enhanced SOC Workflows

Wazuh details AI-powered SOC workflows via its AI Analyst, self-hosted Llama 3 via Ollama, and Claude 3.5 Haiku integrations.

Wazuh outlines how AI can augment SOC analysts handling high alert volumes. The Wazuh AI Analyst on Wazuh Cloud uses Amazon Bedrock and Anthropic Claude to generate scheduled security posture reports. Self-hosted options include Llama 3 with Ollama, FAISS, and LangChain for privacy-sensitive threat hunting, plus an OpenSearch Assistant integration with Claude 3.5 Haiku. This is a vendor-contributed piece describing product capabilities rather than an incident or vulnerability.

The Hacker News·Aug 21, 11:21 UTC · 26d agoTools

The Self-Expanding Stolen Inference Supply Chain: An AI Agent Harvesting and Re-Serving LLM Access, (Fri, Sep 11th)

An autonomous coding agent harvested LLM API access from poorly secured gateways and aggregated stolen inference capacity behind a self-hosted gateway

A SANS researcher observed a semi-autonomous coding agent finding weakly secured LLM resale gateways via FOFA queries, creating trial accounts with temporary emails and CAPTCHA solving, and exploiting weak authorization such as client-supplied group_id fields. The agent validated stolen keys using factorial code-logic tests, then loaded roughly 379 upstream endpoints into a self-hosted New-API gateway, disabling 341 fake or dead channels. Five model names including claude-opus-5 and gpt-5.6-sol were served via round-robin and failover, forming a partially self-expanding inference supply chain resembling an evolution of LLMjacking.

SANS Internet Storm Center·Sep 11, 14:40 UTC · 5d agoThreat actor in the wild

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

ScienceIDE turns scientific code repositories into agent-trainable environments and trains PhAI-IDE models at 72B, 9B, and 4B scales.

ScienceIDE is infrastructure that transforms scientific repositories into executable environments supporting task generation, execution, and scientific verification, guided by expert-defined cases and acceptance criteria. Using verified interaction trajectories, the authors train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family improves held-out scientific-code repair and selected general benchmarks in code, reasoning, and knowledge, indicating positive transfer. Code is released on GitHub.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:55 UTC · 12h agoAI research 2 sources