Evidence Receipt. Related Resources.
Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning
Use This Via API or MCP
Use this Signal Canvas via API or MCP
Route this paper proof surface into REST, MCP, or developer workflows while preserving the same evidence receipt and related-resource context.
Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/overconfident-errors-need-stronger-correction-asymmetric-confidence-penalties-for-reinforcement-learning
- Proof freshness
- stale
- Proof status
- unverified
- Display score
- 5/10
- Last proof check
- 2026-04-02
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 0
- Source count
- 0
- Coverage
- 17%
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning
Canonical ID overconfident-errors-need-stronger-correction-asymmetric-confidence-penalties-for-reinforcement-learning | Route /signal-canvas/overconfident-errors-need-stronger-correction-asymmetric-confidence-penalties-for-reinforcement-learning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/overconfident-errors-need-stronger-correction-asymmetric-confidence-penalties-for-reinforcement-learningMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "overconfident-errors-need-stronger-correction-asymmetric-confidence-penalties-for-reinforcement-learning",
"query_text": "Summarize Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning",
"normalized_query": "2602.21420",
"route": "/signal-canvas/overconfident-errors-need-stronger-correction-asymmetric-confidence-penalties-for-reinforcement-learning",
"paper_ref": "overconfident-errors-need-stronger-correction-asymmetric-confidence-penalties-for-reinforcement-learning",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Preparing verified analysis
Dimensions overall score 5.0
GitHub Code Pulse
No public code linked for this paper yet.
CLAIM MAP
No public claim map is available for this paper yet.
