Datasets:
prompt stringlengths 3.53k 14.6k | response stringlengths 722 9.13k |
|---|---|
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper provides the first comprehensive numerical evidence that the cavity method’s replica‑symmetric predictions are exact for the random link TSP in d=1, d=2, and in the large‑d limit, thereby supporting the validity of replica symmetry for this NP‑hard problem. It also uncovers an unexpected ex... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper claims that jointly learning word embeddings and a neural language model yields a substantial reduction in perplexity (20–35 % better than state‑of‑the‑art smoothed trigram models) on both medium and large corpora, demonstrating that distributed representations can effectively combat the cu... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper introduces slice sampling as a versatile, automatically adaptive MCMC framework that requires only the ability to evaluate the target density (and optionally its gradient). It demonstrates that slice sampling can be implemented with minimal tuning, can adapt to local scale and dependencies,... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The ML approach yields statistically efficient, unbiased, and consistent estimators that dramatically reduce statistical errors compared to conventional tomography, enabling accurate reconstruction of quantum states and device parameters with far fewer data points while automatically enforcing physic... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper resolves an open question from Heinrich (2001) by establishing tight (up to logarithmic factors) query complexity bounds for the mean of p‑summable sequences when 1 ≤ p < 2, without any additional restrictions. It provides an explicit quantum algorithm that matches these bounds, thereby com... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "Path integration becomes tractable on a quantum computer (polynomial cost in ε⁻¹), achieving roughly a quadratic speed-up over classical randomized algorithms (e.g., Monte Carlo) and an exponential speed-up over classical worst-case deterministic algorithms.", "core_idea": "Use quantum summation (bas... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper introduces a general framework for quantum algorithms on numerical problems (extending the binary query model to real/complex-valued functions). It develops novel quantum algorithms for summation of p-summable sequences (beyond the previously studied bounded case) and for integration in Leb... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "This local adiabatic evolution achieves the optimal √N scaling for quantum search—matching Grover’s algorithm—while previous global‑rate adiabatic approaches required O(N) time. The paper also proves that no other schedule can beat this √N bound, establishing the method’s optimality.", "core_idea": "... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "A radical reformulation of quantum gravity that removes space-like separation. The approach uniquely predicts both the dimensionality of spacetime (N=4) and the specific matter content (the 3:2 fermion-boson ratio of the Standard Model coupled to gravity) as necessary conditions for a consistent fiel... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "Proposes a scalable, regular architecture (stacked square grids of switchable couplings) that requires only global field manipulation and offline programming of couplings, eliminating need for local coherent operations or tunable couplings during computation.", "core_idea": "A scalable adiabatic quan... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "Proposes SMC samplers as a new class of algorithms for sequential sampling from arbitrary sequences of distributions, generalizing previous SMC methods (which required nested spaces) and connecting to Annealed Importance Sampling. Introduces a nonlinear Feynman-Kac flow interpretation and a nonlinear... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The algorithm achieves modularity‑based community detection thousands of times faster than the Girvan–Newman method, scales to networks with up to ~10^6 vertices, retains comparable or superior accuracy on synthetic and real data, and naturally extends to weighted networks while providing a full dend... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper provides a novel Bayesian interpretation of the Maximum Probability method by framing it as a MAP estimation problem over empirical types, rather than parameters. It then uses the Maximum Probability Theorem to show that this Bayesian interpretation asymptotically applies to the Maximum Ent... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper is the first to compute exact asymptotic values of maximum weight independent sets and matchings in sparse random graphs for a broad class of weight distributions (e.g., exponential), even when c > e or r > 3, where the unweighted case remains open. It introduces a local optimality propert... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper extends prior integer-moment results (e.g., Pitman's o(n^{-(r-1)}) bound for finite r-th moment) to any real ergodic degree d > 0, proving sharp subgeometric convergence of order n^{-d}. It provides both upper and lower bounds under explicit conditions on the initial distribution's P-order.... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper generalizes additive pattern database heuristics beyond sliding‑tile puzzles to new domains (4‑peg Towers of Hanoi and optimal vertex cover), introduces the first dynamic partitioning technique for additive pattern databases, and demonstrates that these heuristics are the best known admissi... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper claims that (1) the BB‑wise mutation operator scales as O(√k log m) faster than a standard select‑recombine PMBGA, providing a speed‑up of O(√k log m) for additively separable problems, and (2) the endogenous probabilistic fitness model reduces the number of actual evaluations to 1–10 % of ... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper develops and applies number-theoretic methods, particularly based on heights and field extensions, to count flux vacua with special properties (W = 0, discrete symmetries) that are not accessible via continuous flux approximations. It provides explicit counts and scaling laws (e.g., L^2 for... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The method achieves identical community partitions as the earlier greedy modularity algorithm but with a dramatically reduced runtime—essentially linear for sparse, hierarchical networks—by exploiting sparsity and efficient data structures, enabling analysis of networks with hundreds of thousands to ... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper claims a novel, unifying framework that links the average size of a DPLL search tree to the powers of an evolution operator, enabling an exact expression for the expected tree size. It introduces the dynamical‑annealing approximation and the resulting PDE, which accurately predict the expo... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper provides the first detailed mathematical analysis of the uniform local heuristic on random graphs, deriving explicit expressions for the size of the giant component, expected message count, average node degree, and path stretch. It demonstrates that such simple local rules can produce near‑... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The procedure automatically achieves the optimal minimax estimation rate over a range of sparsity classes (nearly black and ℓp balls) and loss functions (q ∈ (0,2]), without prior knowledge of the sparsity level. It combines empirical Bayes weight estimation with a heavy-tailed prior to ensure bounde... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper introduces a new computational model— a quantum‑chaos computer that couples a standard quantum circuit with a chaotic dynamical amplifier— and claims that this model can solve NP‑complete problems, such as SAT, in polynomial time, thereby surpassing the limitations of conventional quantum c... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The paper presents the first analytical framework for a degree‑based probabilistic flooding scheme (heuristic flooding), showing through generating‑function analysis and extensive simulations on Poisson and power‑law random graphs that it achieves a superior trade‑off: lower message overhead than pro... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "By augmenting standard unitary quantum computation with a nonlinear chaotic amplifier or with state‑adaptive stochastic‑limit dynamics, the paper claims it is possible to distinguish a vanishingly small amplitude from zero in polynomial time, thereby solving SAT—and by extension all NP‑complete probl... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The model unifies existing contagion theories, predicts a distinct intermediate dynamical class, and introduces measurable indicators for population susceptibility and control strategies, offering a novel, generalizable approach to studying and managing contagion events.", "core_idea": "A generalized... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "The work claims a universal classification of contagion dynamics into three distinct regimes, provides new empirical metrics for assessing population susceptibility, and suggests strategies for either inhibiting or facilitating large contagion events.", "core_idea": "Introduce a memory‑dependent cont... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "VAL is the first automatic validation tool to handle continuous effects in PDDL, providing a rigorous semantics, efficient ODE solving, invariant checking over continuous time, and a structured plan‑repair advice system that supports mixed‑initiative planning in real‑world domains such as space opera... |
You are a research assistant specializing in computer algorithms.
Your task is to apply a mechanism-level method delta to an existing algorithm and produce the improved algorithm.
You must:
1. Faithfully apply the given method delta.
2. Preserve the research problem context unless the delta explicitly shifts it.
3. Pro... | {"novelty_claim": "This work is the first to provide rigorous theoretical performance guarantees for auction-based multi‑robot routing across a broad set of bidding rules and team objectives. It establishes upper bounds (e.g., 2 for MINISUM, 2n for MINIMAX, 2m for MINIAVE) and matching lower bounds, demonstrating that ... |
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PCF Method Delta SFT Data
This dataset contains supervised fine-tuning examples for Past Creates Future (PCF), a research workflow for generating algorithmic ideas by learning method-level deltas between related algorithms.
Each example is a JSON object with:
prompt: an instruction plus an existing algorithm description and a method delta.response: the target improved algorithm description in JSON form.
Files
| File | Rows | Purpose |
|---|---|---|
pcf_detail_to_detail_30k_conf0p6.jsonl |
5,067 | Main dataset used for detail-to-detail SFT. The prompt and response use richer method-detail fields. |
pcf_compact_30k_conf0p6.jsonl |
5,067 | Compact baseline dataset. The target focuses on novelty_claim, core_idea, and algorithm_description. |
Intended Use
The dataset is intended for research on:
- algorithm idea generation;
- method-delta transfer;
- supervised fine-tuning of research assistant models;
- comparing compact algorithm generation against richer detail-to-detail generation.
The dataset is not intended to reproduce source papers verbatim. It contains structured training examples derived from method summaries and method-delta summaries.
Provenance
The examples were generated from a local corpus of algorithm papers using a multi-stage PCF preprocessing pipeline:
- Extract method summaries from paper text.
- Extract or summarize method-level deltas between related algorithm pairs.
- Combine existing-method detail, delta detail, and target-method detail into SFT prompt/response examples.
- Filter examples using confidence thresholds and manually maintained exclusion lists for non-method or problematic inputs.
The main detail-to-detail dataset corresponds to the 30k extraction setting and confidence threshold 0.6.
Limitations
- The dataset is derived from automatically extracted summaries, so individual examples may contain extraction or summarization errors.
- Confidence filtering reduces but does not eliminate noisy pairs.
- The dataset is designed for generating candidate research ideas; empirical algorithm performance must still be validated by downstream experiments.
- Licensing of the original papers varies. This dataset is released as structured derived summaries for research use and does not include paper PDFs, full paper text, GROBID XML, or other raw paper artifacts.
Related Models
Models trained from this data include:
ssfc/pcf-qwen3-14b-detail-to-detail-lorassfc/pcf-qwen3.5-9b-detail-to-detail-lorassfc/pcf-ministral3-base-detail-to-detail-lorassfc/pcf-qwen3-14b-compact-30k-conf0p6-lora
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