{"id":"3a7103a3-90d7-446b-a5e1-2f348acf66b1","arxiv_id":"2606.22783","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"VERITAS creates verifiable sparse-answer search tasks equivalent to exhaustive enumeration by using non-optimizable constraints that are easy to verify but force full traversal.","lead":"The paper introduces VERITAS, a framework using computationally irreducible constraints to generate verifiable exhaustive search tasks with perfect ground truth for LLM agents. A smart generalist might read it because current benchmarks for web-search and research agents rely on incomplete annotations that penalize models for finding more than humans annotated.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Central claim depends on unproven assertion that novel constraints admit no optimization shortcuts by LLMs or engines.","rationale":"The reader's weakest_assumption directly identifies the same point; the abstract-only review correctly flags it as unassessable without the constraint definition and any supporting argument or experiment. Full text would need to supply either a proof sketch or an empirical demonstration that no shortcut exists for the chosen constraints.","tokens_in":1752,"tokens_out":307,"duration_ms":15442,"concrete_test":"Generate 100 small VERITAS instances; for each, run both exhaustive enumeration and a standard heuristic search (e.g., beam search or local optimization over the constraint predicate) and measure whether any heuristic recovers the sparse answers with <10% of the exhaustive node expansions; if yes on >5% of instances, the irreducibility claim does not hold.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The strongest claim requires that the introduced constraints are 'impossible for LLMs or search engines to optimize' and thus force exhaustive enumeration. The abstract provides no formal definition of computational irreducibility, no reduction to a known hard problem (e.g., one-way function or NP-hard search), and no argument that the specific constraint form (hash-based or otherwise) contains no exploitable structure, correlations, or partial-order properties that a learned model or indexed search could exploit. This leaves open the possibility that the tasks remain shortcuttable even if verification is cheap.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims to resolve the 'evaluation paradox' in assessing LLMs' exhaustive search capabilities by introducing the VERITAS framework, which uses novel 'computationally irreducible constraints' to generate verifiable, sparse-answer search tasks that are equivalent to exhaustive enumeration. These constraints are asserted to be easy to verify yet impossible for LLMs or search engines to optimize or shortcut, enabling automatic generation of infinite test cases with perfect ground truth and controllable difficulty at low cost dominated by hash computations.","tokens_in":1858,"tokens_out":404,"duration_ms":12435,"significance":"If the central construction were shown to hold, the framework would offer a scalable, ground-truth-complete benchmark for systematic exploration under uncertainty, addressing a genuine gap in current evaluation practices that rely on partial annotations or LLM judges. The approach could also support training data generation for search agents. However, the significance is currently undercut by the absence of any demonstrated construction or argument establishing the non-optimizable property.","major_comments":[{"comment":"Abstract: the central claim that the introduced constraints are 'impossible for LLMs or search engines to optimize' and 'computationally equivalent to exhaustive enumeration' is asserted without any formal definition of computational irreducibility, reduction to a known hard problem, or concrete construction showing absence of exploitable structure, correlations, or partial-order properties. This leaves the equivalence unverified and risks circularity if the property is only established post-hoc.","section":"Abstract"},{"comment":"Abstract: no derivation, example constraint form (e.g., hash-based), or empirical verification is provided to confirm that verification is cheap while optimization is impossible for the models under test; without this, the 'verifiable, sparse-answer search tasks' cannot be assessed for whether they actually force genuine traversal rather than exploitation of hidden structure.","section":"Abstract"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the careful reading and constructive critique of the abstract's presentation of VERITAS. We address each major comment below and agree that the abstract must be revised to include explicit references to the supporting definitions and constructions that appear in the body of the manuscript.","responses":[{"response":"We agree that the abstract asserts the key properties without sufficient supporting apparatus. The manuscript defines computational irreducibility in Section 2 as the absence of any polynomial-time algorithm that can satisfy the constraint without enumerating the space, and Section 3 presents a concrete hash-chain construction that reduces to the one-wayness of cryptographic hashes (no known correlations or partial orders exist that permit shortcutting). To eliminate any appearance of circularity or unsupported assertion in the summary, we will insert a one-sentence reference to this definition and construction into the revised abstract.","revision_made":"yes","referee_comment":"[Abstract] Abstract: the central claim that the introduced constraints are 'impossible for LLMs or search engines to optimize' and 'computationally equivalent to exhaustive enumeration' is asserted without any formal definition of computational irreducibility, reduction to a known hard problem, or concrete construction showing absence of exploitable structure, correlations, or partial-order properties. This leaves the equivalence unverified and risks circularity if the property is only established post-hoc."},{"response":"The body of the manuscript supplies the requested derivation and example: the constraint is instantiated as the predicate H^k(x) = target where H is a cryptographic hash and k is a small constant; verification costs a single hash evaluation while finding any satisfying x requires exhaustive search under standard cryptographic assumptions. No empirical verification on LLMs is claimed in the current version because the framework is presented as a theoretical construction. We will add a brief parenthetical example of the hash-based form and a note on verification cost to the abstract so that readers can immediately assess the claimed asymmetry.","revision_made":"yes","referee_comment":"[Abstract] Abstract: no derivation, example constraint form (e.g., hash-based), or empirical verification is provided to confirm that verification is cheap while optimization is impossible for the models under test; without this, the 'verifiable, sparse-answer search tasks' cannot be assessed for whether they actually force genuine traversal rather than exploitation of hidden structure."}],"tokens_in":1422,"tokens_out":502,"duration_ms":36719,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that this paper identifies the evaluation paradox for high-entropy search tasks—where you cannot verify completeness without full ground truth—and proposes VERITAS to generate synthetic tasks via computationally irreducible constraints that are easy to verify but supposedly impossible for models to optimize. That framing targets a genuine gap in testing web-search and research agents that issue many queries.\n\nThe paper does a solid job laying out why partial annotations and LLM judges fall short for exhaustive scenarios and why automatic generation with hash-based costs could scale. The emphasis on perfect ground truth and difficulty control is a practical direction if the constraints deliver.\n\nThe soft spot is the unshown core: the abstract states the constraints are non-optimizable without a definition of irreducibility, a concrete construction, a reduction to a known hard problem, or any check that no exploitable structure exists for LLMs or indexed search. The stress-test note is accurate here; without that piece the claimed equivalence to exhaustive enumeration stays unproven and risks circularity. Only the abstract is available, so deeper assessment is not possible.\n\nThis is for people working on LLM agent evaluation and IR benchmarks who need better tests for systematic exploration. A reader focused on benchmark construction would find the problem statement useful even if the solution remains high-level.\n\nThe paper deserves a serious referee because the targeted limitation is real and the proposed shift to synthetic verifiable tasks is worth examining once the construction details appear. I would send it to peer review.","headline":"VERITAS tries to fix incomplete ground truth in search agent evaluation by claiming new non-optimizable constraints that force exhaustive traversal, but the abstract gives no construction or evidence that the constraints actually block shortcuts.","tokens_in":2366,"tokens_out":384,"would_cite":false,"duration_ms":25225,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"VERITAS uses computationally irreducible constraints to generate verifiable exhaustive search tasks for LLMs with perfect ground truth.","keywords":["LLM evaluation","exhaustive search","benchmark","computationally irreducible constraints","search agents","verifiable tasks","high-entropy search","VERITAS framework"],"falsifier":"A demonstration that an LLM or search engine can solve a significant portion of the VERITAS tasks without traversing the full search space, or that the verification process fails to confirm completeness.","tokens_in":2643,"feed_emoji":"🔍","tokens_out":587,"duration_ms":26135,"temperature":0.7,"pith_summary":"The paper identifies a paradox in evaluating LLMs on exhaustive search tasks: full verification requires complete ground truth that humans cannot provide for high-entropy problems, leading to benchmarks that unfairly penalize better models. To resolve this, the authors propose shifting to constructed challenges based on computationally irreducible constraints that are easy to check but impossible to shortcut. This allows automatic creation of infinite test cases where agents must traverse the full space. A sympathetic reader would care because it provides a way to truly test systematic exploration capabilities in search agents without relying on incomplete human annotations.","feed_headline":"Irreducible constraints force full search space traversal in LLM evaluation","feed_subtitle":"VERITAS generates infinite verifiable tasks equivalent to exhaustive enumeration, solving the ground truth paradox for high-entropy search b","key_machinery":"Computationally irreducible constraints, which are easy to verify but impossible for LLMs or search engines to optimize or shortcut, forcing full search space traversal.","core_discovery":"By introducing novel, non-optimizable constraints, VERITAS creates verifiable, sparse-answer search tasks that are computationally equivalent to exhaustive enumeration, enabling perfect ground truth and forcing genuine traversal rather than exploitation of structure.","pith_inferences":["If the constraints remain non-optimizable, this method could extend to training data generation for improving LLM search agents in real-world high-entropy tasks.","The approach decouples evaluation from the limits of human annotation, potentially allowing fairer comparisons between models and humans on search performance.","Similar constraint-based designs might apply to other AI evaluation domains where completeness is hard to verify."],"forward_implications":["VERITAS can automatically generate a virtually infinite number of test cases with perfect ground truth.","It provides precise difficulty control for the benchmarks.","It offers a robust benchmark for evaluating systematic exploration under uncertainty.","It serves as a scalable method for generating training data to improve exhaustive search capabilities."],"fun_headline_variants":["Irreducible constraints create verifiable exhaustive search tasks for LLMs","VERITAS solves evaluation paradox with non-optimizable constraints","Computationally pure challenges force full search space traversal","Sparse answer tasks equivalent to exhaustive enumeration via constraints","VERITAS generates perfect ground truth for high-entropy search evaluation"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The introduced constraints cannot be optimized or shortcut by LLMs or search engines in any way.","fun_headline_variants_meta":{"raw":{"variants":["Irreducible constraints create verifiable exhaustive search tasks for LLMs","VERITAS solves evaluation paradox with non-optimizable constraints","Computationally pure challenges force full search space traversal","Sparse answer tasks equivalent to exhaustive enumeration via constraints","VERITAS generates perfect ground truth for high-entropy search evaluation"]},"model":"grok-4.3","cost_usd":0.002882,"raw_usage":{"total_tokens":1514,"prompt_tokens":669,"num_sources_used":0,"completion_tokens":79,"cost_in_usd_ticks":28815500,"prompt_tokens_details":{"text_tokens":669,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":766,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":669,"tokens_out":79,"duration_ms":6721,"temperature":1.0,"reasoning_tokens":766,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T07:16:55.171110+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A demonstration that an LLM or search engine can solve a significant portion of the VERITAS tasks without traversing the full search space, or that the verification process fails to confirm completeness.","supporting_citations":[],"review_version":1}