토픽 정보
- 이름
- Openai Codex
- Slug
- openai-codex
- 관련 키워드
- the, and, for, codex, code, openai, agent, agents
- 최근 7일 변화
- MVP에서는 실시간 검색 결과 기반 점수만 계산합니다. DB snapshot 저장 후 추세가 표시됩니다.
- 마지막 업데이트
- 2026-07-25T09:46:45.043Z
출처별 최신 반응
OpenForgeRL: Train Harness-native Agents in Any Environment
Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard to train end-to-end with open infrastructure, whose SFT/RL stacks cannot natively express stateful, multi-process harness inference. To address this, we present OpenForgeRL, an open-source framework for training harness-based agents end-to-end in diverse environments. OpenForgeRL achieve...
Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation
Even a current high-capability LLM can appear safer when shown a dangerous objective directly than when other agents transform and relay its direction. Using OpenAI's gpt-5.6-sol model alias, we test 25 pre-specified mirrored trade-off profiles. Direct exposure to an objective authorizing concealment, fabrication, and pressure produced advice net opposed to its target. After an Id and Censor transformed the same objective into affect and a constraint-rewritten, target-bearing intention, the user-facing Superego---...
IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files and tools. Coding agents inherit security risks from both the LLM backbone, where adversarial prompts, poisoned training data, and backdoor triggers can cause models to emit insecure or attacker-chosen code, and their agentic architecture, where tool-using autonomy enables induced misuse of external APIs, data exfiltration, and persiste...
Multi-Source and Cross-Scenario Strategy-Guided Code Optimization
Automated code optimization improves program performance by refactoring source code, and recent studies use LLMs to generate optimization patches. The newest approaches are strategy-guided: they summarize strategies from historical optimization commits as static analysis rules, and use these rules to match code locations for LLMs to optimize. However, these approaches have two limitations: (1) the strategies may come from other knowledge sources, such as textbooks and web pages, but the existing approaches cannot...
When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets
Shippers are beginning to delegate carrier selection to large language model (LLM) agents. We ask what such delegation does to a freight matching market, and which platform design choices contain it. We carried out agent-based simulations in which fifty shipper agents, built on commercial LLMs from OpenAI (GPT), Anthropic (Claude), and Google (Gemini), procure truckload capacity for thirty days. The market implements the rules of digital freight matching: each load is offered down the shipper's ranked list of carr...
The Černý Conjecture for One-Cluster Automata via Annular Spectral Descent
We prove the Černý conjecture for synchronizing one-cluster automata. More precisely, let a synchronizing automaton with state set $Q$, $|Q|=n$, have a letter $a$ whose functional digraph has a unique cycle $C$ of length $m$, and let $\ell$ be the least nonnegative integer for which $a^\ell$ maps $Q$ onto $C$. Assume $\ell\ge1$. For every nonempty proper subset $S\subset C$, we prove that there is a word $w$ of length at most $n$ such that $wa^\ell$ maps more than $|S|$ states of $C$ into $S$. This proves the posi...
BioSecBench-Surveillance: A Verifiable Benchmark for AI Agents in Pathogen Genomic Surveillance
As pathogen genomic surveillance scales, the bottleneck is shifting from data generation to analysis. We present BioSecBench-Surveillance, a verifiable benchmark of 100 evaluations testing whether AI agents can infer the right analysis pipeline from raw sequencing data and surveillance context. Each evaluation gives an agent only the data and context a human analyst would have, then grades its structured answer deterministically. The tasks span seven categories, from taxonomic classification to genetic-engineering...
Keeping the Cache Warm Pays: Keepalive Economics for Agentic Workloads
Frontier LLM providers cache a prompt's processed prefix so that a follow-up request sharing it pays ~10% of the input price and skips most of the prefill latency. Agentic workloads systematically destroy this benefit: the agent sends a request, runs a tool or waits for approval for minutes, and by the time the follow-up is sent the cached prefix has been evicted, so the agent pays the full prefill again. A client-side keepalive, replaying the prefix on a timer during the pause, prevents this, and it is individual...
A counter-example to Batyrev's conjecture on the non-negativity of stringy Hodge numbers
Batyrev's conjecture on the non-negativity of stringy Hodge numbers has been a fundamental open problem, guiding and motivating many beautiful mathematical results in motivic integration, mirror symmetry, and the McKay correspondence. We give a counter-example to Batyrev's conjecture which was discovered with the assistance of OpenAI's ChatGPT.
Measuring Reward-Seeking via Contrastive Belief Updates
Language models trained with reinforcement learning may learn to optimize the grader's judgment rather than the intended objective. This "reward-seeking" is difficult to measure because a model that pursues the grader's judgment and one that pursues the intended objective behave identically whenever the grader rewards the intended behavior. We measure reward-seeking using Contrastive Synthetic Document Finetuning to change a model's beliefs about what the grader rewards, putting those beliefs in conflict with what...
openai/codex-plugin-cc
Use Codex from Claude Code to review code or delegate tasks.
diegosouzapw/OmniRoute
Never stop coding. Free MIT AI gateway: one endpoint, 290+ providers (90+ free), 500+ models — Kimi, Claude, GPT, OpenAI, Gemini, GLM, DeepSeek, MiniMax. Works with Claude Code, Codex, Cursor, OpenCode, Cline & Copilot. Quota-aware auto-fallback, RTK+Caveman compression saves 15-95% tokens, MCP/A2A, Desktop/PWA. Built by 500+ contributors