{"id":"4ebc19c4-4830-41eb-8c59-0b5b06ae022d","arxiv_id":"2606.23136","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"LLM-generated waypoints guided by landmark distances reduce A* node expansions by ~50% in non-geometric graphs up to 2000 nodes with only marginal path cost increase.","lead":"The paper proposes an LLM-aided A* search that uses large language models to generate waypoints guided by landmark distances as structural features for non-geometric network graphs. A smart generalist might read it to see how AI can augment classical algorithms for routing problems with arbitrary costs like latency.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"LLM waypoint generation requires encoding graph structure via landmark distances in prompts, which for 2000 nodes may cause high token usage and overhead not accounted for in the expanded nodes metric.","rationale":"Matches the reader's weakest assumption on overhead from LLM calls. The concrete mechanism of supplying structural features to the LLM is the point where this assumption is least secure, as prompt engineering analysis does not address token counts or latency. The verdict moves to CONDITIONAL because the empirical claim on expansions may hold but practical utility requires the overhead check.","tokens_in":1689,"tokens_out":322,"duration_ms":48342,"concrete_test":"For the largest reported graph, reconstruct the prompt template used to supply landmark distances and count tokens for a typical input; if >4k tokens or if multiple calls are needed, measure wall-clock time of the full LLM-aided A* versus plain ALT A* to check if savings in expansions translate to time savings.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"To allow the LLM to select a meaningful waypoint, the prompt must convey which nodes are available and their landmark distance vectors. For graphs with 2,000 nodes this entails a substantial data payload per LLM call. The paper reports reduced expansions but does not quantify prompt sizes, number of LLM calls per query, or total runtime. If the LLM is called once per search, token costs and latency could still dominate for repeated queries; if called more frequently the problem worsens. This directly challenges whether the approach delivers net efficiency gains even if expansions drop 50%.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes an LLM-aided A* search algorithm for shortest paths in non-geometric network graphs. Landmark distances serve dual roles as an admissible ALT heuristic and as compact structural features fed to an LLM to generate waypoints that guide expansion. Experiments on multiple topologies with up to 2,000 nodes are reported to show an approximately 50% reduction in expanded nodes with only marginal path-cost increase relative to optimal; prompt engineering is also analyzed, with the conclusion that heuristic features outperform advanced prompting techniques.","tokens_in":1824,"tokens_out":409,"duration_ms":31426,"significance":"If the efficiency claims can be substantiated with complete experimental protocols and overhead measurements, the work would offer a concrete demonstration of hybrid LLM-classical search for domains lacking geometric structure, with potential relevance to network routing and optimization. The absence of runtime accounting and statistical validation currently prevents assessment of whether net gains are realized.","major_comments":[{"comment":"Abstract and Experiments section: the central claim of ~50% reduction in expanded nodes with marginal cost increase is presented without any description of graph generation procedures, number of queries or trials per topology, choice of baselines (standard A*, pure ALT, etc.), error bars, or statistical significance tests. This directly undermines evaluation of the reported improvement.","section":"Abstract / Experiments"},{"comment":"Experiments section: no quantification is given of LLM call frequency, prompt token counts (especially the landmark-distance vectors for 2,000-node graphs), or total wall-clock time including inference latency. Without these metrics the net efficiency gain cannot be established even if node expansions drop.","section":"Experiments"}],"minor_comments":[{"comment":"Notation for landmark distances and waypoint selection could be formalized with a short pseudocode or equation block to clarify how the LLM output is integrated into the A* open set.","section":"Method"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback on our experimental presentation. We address each major comment below and will revise the manuscript accordingly to improve clarity and substantiation of results.","responses":[{"response":"We agree that additional experimental details are needed for full reproducibility and evaluation. In the revised manuscript we will expand the Experiments section with: explicit graph generation procedures for each topology, the exact number of queries and independent trials per topology, a complete list of baselines (standard A*, pure ALT, and others), error bars on all reported metrics, and statistical significance tests (e.g., paired t-tests) supporting the ~50% node-expansion reduction. The abstract will remain unchanged as it accurately summarizes the findings once these details are provided.","revision_made":"yes","referee_comment":"[Abstract / Experiments] Abstract and Experiments section: the central claim of ~50% reduction in expanded nodes with marginal cost increase is presented without any description of graph generation procedures, number of queries or trials per topology, choice of baselines (standard A*, pure ALT, etc.), error bars, or statistical significance tests. This directly undermines evaluation of the reported improvement."},{"response":"The referee is correct that overhead accounting is required to confirm net gains. We will add to the revised Experiments section a dedicated analysis quantifying LLM call frequency per search, average and maximum prompt token counts (noting that the landmark-distance vectors are compact and scale linearly), and total wall-clock runtime decomposed into classical search time and LLM inference latency. These measurements will allow readers to assess whether the reduction in node expansions translates to practical efficiency improvements.","revision_made":"yes","referee_comment":"[Experiments] Experiments section: no quantification is given of LLM call frequency, prompt token counts (especially the landmark-distance vectors for 2,000-node graphs), or total wall-clock time including inference latency. Without these metrics the net efficiency gain cannot be established even if node expansions drop."}],"tokens_in":1349,"tokens_out":423,"duration_ms":22171,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's core move is to feed landmark distance vectors to an LLM so it can propose waypoints that steer A* on graphs where no geometric heuristic exists. That combination is not a standard extension of either ALT or prior LLM search work, and the experiments show it cuts expanded nodes by about 50% across several topologies up to 2000 nodes while keeping path cost close to optimal. They also test that adding the landmark features beats fancier prompt engineering, which is a useful practical result.\n\nThe experiments are run on multiple graph families, which is better than a single synthetic case. The dual role of the landmarks (heuristic plus LLM input) is cleanly motivated and avoids obvious circularity.\n\nThe main gap is runtime. The stress-test concern about prompt size holds up from the abstract: for 2000 nodes the landmark vectors will make each prompt large, yet the paper only reports node expansions and path cost, not number of LLM calls, token counts, or wall-clock time. If the LLM is invoked once per query the latency could dominate; if more often it gets worse. Without those numbers the 50% expansion claim does not yet translate to a net efficiency gain.\n\nThe work is aimed at people doing pathfinding in latency or cost-based networks who are already willing to experiment with LLM calls. A referee should see the full methods to check graph generation details, exact baselines, and whether any end-to-end timing was collected. It is worth sending out for review because the idea is concrete and the reported reduction is large enough to test further, even if the overhead question needs to be answered.","headline":"LLM waypoints halve A* expansions on non-geometric graphs but the unmeasured LLM call overhead looks like the real limit.","tokens_in":2284,"tokens_out":396,"would_cite":false,"duration_ms":18830,"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":"LLM-generated waypoints reduce A* node expansions by about 50% in non-geometric network graphs.","keywords":["A* search","LLM-aided search","non-geometric graphs","landmark distances","network optimization","waypoints","shortest path"],"falsifier":"A counterexample would be a collection of test graphs where the LLM waypoints cause A* to expand more nodes than the baseline or produce paths whose cost exceeds the optimal cost by more than a small margin.","tokens_in":2602,"feed_emoji":"🧭","tokens_out":592,"duration_ms":24336,"temperature":0.7,"pith_summary":"The paper proposes using an LLM to generate intermediate waypoints that steer A* search in graphs where edge weights represent arbitrary costs rather than spatial distances. Landmark distances serve both as an admissible heuristic for the search itself and as compact features fed to the LLM so it can produce waypoints that restore a useful distance-to-destination signal. Experiments across multiple topologies with up to 2,000 nodes show the LLM waypoints cut expanded nodes by roughly half while the final path cost stays close to optimal. The work further reports that feeding heuristic estimates to the LLM outperforms advanced prompting techniques.","feed_headline":"LLM waypoints halve A* expansions in non-geometric networks","feed_subtitle":"Landmark distances restore the distance signal, cutting node expansions by half with near-optimal paths.","key_machinery":"Landmark distances, used both as an admissible ALT heuristic for A* and as structural features supplied to the LLM to generate guiding waypoints.","core_discovery":"An LLM can generate waypoints from landmark distance features that guide A* expansions in non-geometric graphs, reducing the number of expanded nodes by around 50% while incurring only a marginal path cost increase compared to the optimal solution.","pith_inferences":["The technique could be evaluated on graphs larger than 2,000 nodes or on real network topologies to test scaling behavior.","Caching waypoint suggestions for repeated landmark patterns might offset the cost of LLM calls in repeated queries.","Similar waypoint guidance could be explored for other informed search algorithms or for dynamic networks where edge costs change."],"forward_implications":["LLM-generated waypoints reduce the number of expanded nodes by around 50%.","The approach incurs only a marginal path cost increase compared to the optimal solution.","Incorporating compact structural features such as heuristic estimates is more effective than advanced prompting techniques.","The results hold on multiple graph topologies with up to 2,000 nodes."],"fun_headline_variants":["LLM halves A* expansions with waypoints in networks","Landmark LLM aids A* by halving expansions in graphs","A* expansions cut 50% by LLM waypoints on non-geometric graphs","LLM uses landmarks to guide A* and reduce expansions by half"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Landmark distances supplied to the LLM will consistently produce waypoints that meaningfully reduce expansions without violating A* admissibility or introducing unacceptable computational overhead from LLM calls.","fun_headline_variants_meta":{"raw":{"variants":["LLM halves A* expansions with waypoints in networks","Landmark LLM aids A* by halving expansions in graphs","A* expansions cut 50% by LLM waypoints on non-geometric graphs","LLM uses landmarks to guide A* and reduce expansions by half"]},"model":"grok-4.3","cost_usd":0.004285,"raw_usage":{"total_tokens":2138,"prompt_tokens":633,"num_sources_used":0,"completion_tokens":71,"cost_in_usd_ticks":42849500,"prompt_tokens_details":{"text_tokens":633,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1434,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":633,"tokens_out":71,"duration_ms":11499,"temperature":1.0,"reasoning_tokens":1434,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T06:25:20.556926+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A counterexample would be a collection of test graphs where the LLM waypoints cause A* to expand more nodes than the baseline or produce paths whose cost exceeds the optimal cost by more than a small margin.","supporting_citations":[],"review_version":1}