Opportunity summary
Score3.0PainExplore the reliability of LLMs in probabilistic reasoning tasks through a novel benchmark dataset.
Evidence0 refs | 3 sources | 50% coverage
Blockerno shell-level blocker reported
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ARXIV:2606.07515 · AI CAPABILITY EVALUATION · SUBMITTED 08 JUN · 17:14 UTC · FRESHNESS FRESH
ARXIV:2606.07515AI CAPABILITY EVALUATIONSUBMITTED 08 JUN · 17:14 UTCFRESHNESS FRESHLuca Avena · Gianmarco Bet · Bernardo Busoni · arXiv
Explore the reliability of LLMs in probabilistic reasoning tasks through a novel benchmark dataset.
Opportunity summary
Pain Explore the reliability of LLMs in probabilistic reasoning tasks through a novel benchmark dataset.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Explore the reliability of LLMs in probabilistic reasoning tasks through a novel benchmark dataset. We constructed two datasets, respectively a set of standard exercises and a set of counterintuitive exercises, designed to trigger heuristic…
We investigate the probabilistic reasoning capabilities of large language models through a controlled benchmarking study on discrete probability problems. We constructed two datasets, respectively a set of standard exercises and a set of counterintuitive…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Models achieve an average accuracy of 0.96 on standard problems but only 0.59 on counterintuitive ones. Code availability is flagged in the production record;…
AI Capability Evaluation moved forward this cycle; last verified June 2026. Public score 3.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score3.0Analysis summary
Explore the reliability of LLMs in probabilistic reasoning tasks through a novel benchmark dataset.
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Paper Pack
10.48550/arXiv.2606.07515Explore the reliability of LLMs in probabilistic reasoning tasks through a novel benchmark dataset.
Abstract
We investigate the probabilistic reasoning capabilities of large language models through a controlled benchmarking study on discrete probability problems. We constructed two datasets, respectively a set of standard exercises and a set of counterintuitive exercises, designed to trigger heuristic reasoning, and evaluated 8 state-of-the-art models, each tested with and without Chain-of-Thought prompting. Models achieve an average accuracy of 0.96 on standard problems but only 0.59 on counterintuitive ones. We further provide empirical evidence of token bias: performance drops by over 20% when canonical formulations are replaced by disguised variants. Embedding misleading suggestions in the prompt reduces performance by up to 34%, with no model proving immune. Taken together, the reported findings suggest that current LLMs are not yet genuine probabilistic reasoners, despite their success in advanced mathematical problems.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run pending anchorsA parse run id is attached, but no public source anchors are materialized yet.
Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 3.0
PROBLEM
Explore the reliability of LLMs in probabilistic reasoning tasks through a novel benchmark dataset. We constructed two datasets, respectively a set of standard exercises and a set of counterintuitive exercises, designed to trigger heuristic reasoning, and evaluated 8 state-of-th...
METHOD
We investigate the probabilistic reasoning capabilities of large language models through a controlled benchmarking study on discrete probability problems. We constructed two datasets, respectively a set of standard exercises and a set of counterintuitive exercises, designed to t...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Models achieve an average accuracy of 0.96 on standard problems but only 0.59 on counterintuitive ones. Code availability is flagged in the production record; the public repository link still needs proof...
WHY NOW
AI Capability Evaluation moved forward this cycle; last verified June 2026. Public score 3.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 10, "author": "Luca Avena; Gianmarco Bet; Bernardo Busoni", "title": "How reliable are LLMs when it comes to playing dice?", "creation date": null, "modification date": null
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partial
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Concepts
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Explore the reliability of LLMs in probabilistic reasoning tasks through a novel benchmark dataset.
Segment
AI Capability Evaluation
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Commercially relevant
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 3 sources / 50% coverage
fresh
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
fresh
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
fresh
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 3 sources, 50% evidence coverage.
Gaps
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Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
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Gaps
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Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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