Explore/benchmark/ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment
F

Qiuyu Tian, Zequn Liu, Yingce Xia, Haojie Yin, Youyong Kong/ForeSci: Evaluating LLM Agents for Forward-Looking AI Research JudgmentUnknown

AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence. ForeSci contains 500 tasks across four fast-moving AI domains and four decision families. Each task is paired with a cutoff-aligned offline knowledge base; post-cutoff papers are hidden during generation and used only for validation. To avoid random future-event prediction, tasks are derived from pre-cutoff taxonomy branches and evidence signals, and answer-generation backbones are selected to precede the task cutoffs. We evaluate native LLMs, Hybrid RAG, and three research-agent adaptations across four backbones. Results show that explicit evidence organization improves traceability and factual support, but gains depend strongly on the decision family. Diagnostics reveal a recurring evidence-decision decoupling: agents may cite relevant evidence while forecasting the wrong research object. ForeSci turns forward-looking AI research judgement into a controlled benchmark for evaluating research agents as decision-making systems.

benchmark
GitHubCompare
Refreshed 2mo ago
OverviewActivity52wAlternativesDocs
Stars0
Forks0
HF Downloads—30d
Last commit—
Refreshed2mo ago
Project healthUnknownNo activity data.
Production readinessResearch / EarlyBest for exploration and prototyping.
Risk notesUnknown licenseVerify license before production use.
AgentHub Score
55 / 100
Composite score from 6 signals. How we score →
Active project
55Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 31 days observed0 not enough history
snapshots
Repository activity · 31 days observedReal snapshots from pushed_at
inactivepushed
2026-07-262026-08-27
Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language—
License—
Sourcearxiv
Open source✗ No
Commercial use—
Docs—
Demo—

AgentHub Score

55
Score 55/100
Below average

Alternatives

R
RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems
0 · benchmark
55
S
Security Assessment of DeepSeek Harness with A.I.G: Evaluating Resistance to Indirect Prompt Injection
0 · benchmark
55
A
A Theory of Post-hoc Debate Judgement
0 · benchmark
55
D
Designing a Robust LLM-Based Evaluation System for Agentic AI in Drug Discovery Through Human Alignment
0 · benchmark
55Compare all →

Recent activity

Latest commit ——
Indexed by AgentHub crawler2mo ago
Monitor for new releasesongoing