Tree of Thoughts enables language models to solve complex planning tasks by generating, evaluating, and searching over coherent intermediate thoughts in a tree, raising Game of 24 success from 4% to 74% with GPT-4.
super hub
A formal basis for the heuristic determination of minimum cost paths
20 Pith papers cite this work, alongside 12,265 external citations. Polarity classification is still indexing.
20
Pith papers citing it
12.3k
external citations · external index
hub tools
citation-role summary
background 2
method 1
citation-polarity summary
representative citing papers
cGAN surrogates recover 45-60% of CFD energy savings and high-velocity wake avoidance in 3D AUV path planning while running at 28-146 microsecond inference speeds across 19,800 trajectories.
OSCAR learns class-conditioned survival distributions for obstacle clearance times online (handling right-censored data) to compute patience thresholds in graph-based navigation, converging near oracle performance after few observations per class.
Zeta* and Zeta*-SIPP use elliptical expansion and improved visibility to deliver optimal any-angle paths on grids, with Zeta*-SIPP over 20x faster than prior dynamic planners.
A search-based air traffic conflict-free path planner (SSPP), using three conflict-detection variants, computes routes in milliseconds with straight, aircraft-suitable paths.
The anti-lexicographic SUS-anchor achieves sampling densities less than 1% above the lower bound for alphabet size 4 and k=1, substantially outperforming bidirectional anchors.
Bidirectional Evolutionary Search augments autoregressive expansion with evolutionary recombination operators and dense backward subgoal feedback to produce better candidates than standard best-of-N or tree search for language model self-improvement.
GRAFT-ATHENA projects combinatorial method choices into factored trees that embed as fingerprints in a metric space, enabling an agentic system to accumulate experience across domains and autonomously discover new numerical techniques for physics-informed problems.
CogInstrument represents human reasoning as revisable cognitive motifs in graphical form to support iterative alignment with LLMs during planning tasks, with a N=12 study indicating gains in targeted revision, agency, and trust over standard dialogue interfaces.
A branch-and-bound algorithm with custom node selection, branching rules, and conflict definitions solves the logic-constrained shortest path problem for flight planning with traffic flow restrictions, showing order-of-magnitude speedups on a public global dataset with 20000 real constraints.
FRAMe combines an LLM planner with RAG-based memory and a multi-modal coach agent to generate valid, preference-aligned eVTOL flight plans, achieving up to 93.8% validity across four LLMs.
IR-SIM is a YAML-defined simulator for mobile robot navigation that supports text-prompt scenario creation, policy training, benchmarking, and bridging to higher-fidelity or real-world settings.
Presents a framework for training empirically admissible neural heuristics via underestimating Bellman operator, asymmetric loss, and validation calibration offset, reporting reduced node expansions with no observed admissibility violations on small puzzles.
A hybrid LLM-plus-physics-simulation framework generates synthesis routes for niobium oxides and finds that LLM implicit priors produce more viable plans than classical path-planning algorithms in computational tests.
Convex-Neural RRT* uses neural guidance to predict waypoint regions, extracts convex sampling areas from them, and reports 30-75% faster planning than other neural RRT* methods with ~5% shorter paths and >99% success rate on 18 maps.
Distill refines user task specifications for robots by pruning unnecessary steps, generalizing meanings, and relaxing order constraints, as demonstrated in a crowdsourcing study on a web interface.
The paper argues flat minima are an illusion and that a reparameterization-invariant 'weakness' score predicts generalization where raw sharpness fails.
A hybrid PLL oracle and neural A* method constrains LLM generation to paths in a 700K-node medical graph, claiming better latency-recall tradeoffs and fewer hallucinations than text-only RAG on fertility queries.
A technical review of 32 ARG sampling programs, grouped by algorithm family and compared on event types, performance, usability, and source-code accessibility.
Genetic algorithm with AHP produces more diverse paths than A* whose runtime is independent of environment size, claimed to increase continuum robot resilience in two simulated settings.
citing papers explorer
-
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Tree of Thoughts enables language models to solve complex planning tasks by generating, evaluating, and searching over coherent intermediate thoughts in a tree, raising Game of 24 success from 4% to 74% with GPT-4.
-
3D Underwater Path Planning via Generative Flow Field Surrogates
cGAN surrogates recover 45-60% of CFD energy savings and high-velocity wake avoidance in 3D AUV path planning while running at 28-146 microsecond inference speeds across 19,800 trajectories.
-
OSCAR: Obstacle Survival Curves for Adaptive Robot Navigation
OSCAR learns class-conditioned survival distributions for obstacle clearance times online (handling right-censored data) to compute patience thresholds in graph-based navigation, converging near oracle performance after few observations per class.
-
Optimal any-angle path planning in static and dynamic environments
Zeta* and Zeta*-SIPP use elliptical expansion and improved visibility to deliver optimal any-angle paths on grids, with Zeta*-SIPP over 20x faster than prior dynamic planners.
-
Solution Space Path Planning: A Real-Time Human-Centered Path Planning Algorithm for En-Route Air Traffic Control
A search-based air traffic conflict-free path planner (SSPP), using three conflict-detection variants, computes routes in milliseconds with straight, aircraft-suitable paths.
-
The anti-lexicographic SUS-anchor: a near-optimal k=1 sampling scheme
The anti-lexicographic SUS-anchor achieves sampling densities less than 1% above the lower bound for alphabet size 4 and k=1, substantially outperforming bidirectional anchors.
-
Self-Improving Language Models with Bidirectional Evolutionary Search
Bidirectional Evolutionary Search augments autoregressive expansion with evolutionary recombination operators and dense backward subgoal feedback to produce better candidates than standard best-of-N or tree search for language model self-improvement.
-
GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms
GRAFT-ATHENA projects combinatorial method choices into factored trees that embed as fingerprints in a metric space, enabling an agentic system to accumulate experience across domains and autonomously discover new numerical techniques for physics-informed problems.
-
CogInstrument: Modeling Cognitive Processes for Bidirectional Human-LLM Alignment in Planning Tasks
CogInstrument represents human reasoning as revisable cognitive motifs in graphical form to support iterative alignment with LLMs during planning tasks, with a N=12 study indicating gains in targeted revision, agency, and trust over standard dialogue interfaces.
-
Logic-Constrained Shortest Paths for Flight Planning
A branch-and-bound algorithm with custom node selection, branching rules, and conflict definitions solves the logic-constrained shortest path problem for flight planning with traffic flow restrictions, showing order-of-magnitude speedups on a public global dataset with 20000 real constraints.
-
End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent
FRAMe combines an LLM planner with RAG-based memory and a multi-modal coach agent to generate valid, preference-aligned eVTOL flight plans, achieving up to 93.8% validity across four LLMs.
-
IR-SIM: A Lightweight Skill-Native Simulator for Navigation, Learning, and Benchmarking
IR-SIM is a YAML-defined simulator for mobile robot navigation that supports text-prompt scenario creation, policy training, benchmarking, and bridging to higher-fidelity or real-world settings.
-
Learning Empirically Admissible Neural Heuristics for Combinatorial Search
Presents a framework for training empirically admissible neural heuristics via underestimating Bellman operator, asymmetric loss, and validation calibration offset, reporting reduced node expansions with no observed admissibility violations on small puzzles.
-
Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials
A hybrid LLM-plus-physics-simulation framework generates synthesis routes for niobium oxides and finds that LLM implicit priors produce more viable plans than classical path-planning algorithms in computational tests.
-
Convex-Neural RRT*: Fast and Reliable Learning-Guided Sampling for High-Quality Robot Path Planning
Convex-Neural RRT* uses neural guidance to predict waypoint regions, extracts convex sampling areas from them, and reports 30-75% faster planning than other neural RRT* methods with ~5% shorter paths and >99% success rate on 18 maps.
-
Distill: Uncovering the True Intent behind Human-Robot Communication
Distill refines user task specifications for robots by pruning unnecessary steps, generalizing meanings, and relaxing order constraints, as demonstrated in a crowdsourcing study on a web interface.
-
Are Flat Minima an Illusion?
The paper argues flat minima are an illusion and that a reparameterization-invariant 'weakness' score predicts generalization where raw sharpness fails.
-
TTFT-Aware Graph Chain-of-Thought:Distance-Indexed Neural A* for Low-Hallucination Multi-Hop Medical Reasoning
A hybrid PLL oracle and neural A* method constrains LLM generation to paths in a 700K-node medical graph, claiming better latency-recall tradeoffs and fewer hallucinations than text-only RAG on fertility queries.
-
Human Ancestries Simulation and Inference: a Review of Ancestral Recombination Graph-Based Approaches
A technical review of 32 ARG sampling programs, grouped by algorithm family and compared on event types, performance, usability, and source-code accessibility.
-
Increasing Resilience of Continuum Robots via Motion Planning Algorithms
Genetic algorithm with AHP produces more diverse paths than A* whose runtime is independent of environment size, claimed to increase continuum robot resilience in two simulated settings.