AIRA_2: Overcoming Bottlenecks in AI Research Agents

AIRA_2: Overcoming Bottlenecks in AI Research Agents | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/aira-2-overcoming-bottlenecks-in-ai-research-agents

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 16
Source count: 3
Coverage: 67%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID aira-2-overcoming-bottlenecks-in-ai-research-agents | Route /signal-canvas/aira-2-overcoming-bottlenecks-in-ai-research-agents

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/aira-2-overcoming-bottlenecks-in-ai-research-agents

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: 16

Proof: Verification pending

Freshness state: computing

Source paper: AIRA_2: Overcoming Bottlenecks in AI Research Agents

PDF: https://arxiv.org/pdf/2603.26499v1

Source count: 3

Coverage: 67%

Last proof check: 2026-03-31T20:30:20.275Z

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Not build-ready: AIRA_2: Overcoming Bottlenecks in AI Research Agents

/buildability/aira-2-overcoming-bottlenecks-in-ai-research-agents

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Subject: AIRA_2: Overcoming Bottlenecks in AI Research Agents

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

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Insufficient data

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Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
On MLE-bench-30, AIRA$_2$ achieves a mean Percentile Rank of 71.8% at 24 hours - surpassing the previous best of 69.9%
Implicationpartial
This is a direct numerical result stated in the abstract and supported by Figure 1 and Table 1.
Verificationpartial
partial
Evidencepartial
and steadily improves to 76.0% at 72 hours.
Implicationpartial
This is a direct numerical result stated in the abstract and supported by Figure 1 and Table 1.
Verificationpartial
partial
Evidencepartial
We introduce AIRA$_2$, which addresses these bottlenecks through three architectural choices: an asynchronous multi-GPU worker pool that increases experiment throughput linearly
Implicationpartial
The abstract explicitly states this architectural choice as a solution to a identified bottleneck.
Verificationpartial
partial
Evidencepartial
a Hidden Consistent Evaluation protocol that delivers a reliable evaluation signal
Implicationpartial
The abstract explicitly states this architectural choice as a solution to a identified bottleneck.
Verificationpartial
partial
Evidencepartial
and ReAct agents that dynamically scope their actions and debug interactively.
Implicationpartial
The abstract explicitly states this architectural choice as a solution to a identified bottleneck.
Verificationpartial
partial
Evidencepartial
Ablation studies reveal that each component is necessary
Implicationpartial
The abstract mentions ablation studies and their findings regarding the necessity of each component.
Verificationpartial
partial
Evidencepartial
and that the "overfitting" reported in prior work was driven by evaluation noise rather than true data memorization.
Implicationpartial
The abstract explicitly states this finding from the ablation studies.
Verificationpartial
partial
Evidencepartial
with the gap widening to 7.5 Percentile Rank points at 144 GPU-hours.
Implicationpartial
Figure 2(a) directly illustrates this performance difference and the text quantifies it.
Verificationpartial
partial

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AIRA_2: Overcoming Bottlenecks in AI Research Agents

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