id stringclasses 5
values | title stringclasses 5
values | pdf_path stringclasses 5
values | num_references_total int64 30 69 | num_references_related_work int64 0 27 | bibliography_related_work listlengths 0 27 | bibliography_all listlengths 30 69 | related_work_ids listlengths 0 27 | bibliography_all_ids listlengths 30 69 | idea stringclasses 5
values | status stringclasses 1
value | related_work_summaries dict | generated_idea stringclasses 5
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
5t57omGVMw | Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances | data/extract_demo/5t57omGVMw/paper.pdf | 69 | 27 | [
{
"authors": [
"Peter Bartlett",
"Piotr Indyk",
"Tal Wagner"
],
"doi": "",
"ref_id": "b11",
"title": "Generalization bounds for data-driven numerical linear algebra",
"year": "2022"
},
{
"authors": [
"L Michael",
"Eric Parks",
"Greg De Sturler",
... | [
{
"authors": [
"Jacob Abernethy",
"Chansoo Lee",
"Ambuj Tewari"
],
"doi": "",
"ref_id": "b0",
"title": "Fighting bandits with a new kind of smoothness",
"year": "2015"
},
{
"authors": [
"Brandon Amos"
],
"doi": "10.1561/2200000102",
"ref_id": "b1",... | [
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"dee4e36... | IDEA:
The core idea is to frame the problem of adaptively setting parameters for iterative numerical solvers, when solving a sequence of related linear systems, as an online learning problem. By treating solver parameters as "actions" and solver performance (e.g., iteration count) as "rewards," online bandit algorithms... | ok | {
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"339a77d237e51bb6": "IDEA:\nThe core research idea is that even highly volatile, piecewise Lipschitz functions can be effectively optimized in online and private settings if their discontinuities are suff... | IDEA:
The core research idea is to learn a transformation of the parameter space or objective function that actively enhances the "dispersion" of discontinuities in piecewise Lipschitz functions, thereby making them more amenable to provable online and private optimization.
PROBLEM GAP:
Prior work on online and privat... |
7VPTUWkiDQ | Provable Compositional Generalization for Object-Centric Learning | data/extract_demo/7VPTUWkiDQ/paper.pdf | 66 | 20 | [
{
"authors": [
"Luigi Gresele",
"Vincent Julius Von Kügelgen",
"Bernhard Stimper",
"Michel Schölkopf",
"Besserve"
],
"doi": "",
"ref_id": "b43",
"title": "Independent mechanism analysis, a new concept?",
"year": "2021"
},
{
"authors": [
"Kefan Dong... | [
{
"authors": [
"Joshua B Tenenbaum",
"Charles Kemp",
"Thomas L Griffiths",
"Noah D Goodman"
],
"doi": "10.1126/science.1192788",
"ref_id": "b0",
"title": "How to Grow a Mind: Statistics, Structure, and Abstraction",
"year": "2011"
},
{
"authors": [
"E J ... | [
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"a140b19... | IDEA:
To achieve provable compositional generalization in object-centric learning, an autoencoder must ensure its decoder possesses specific structural properties (compositionality and additivity) and its encoder is explicitly regularized to maintain consistency (invertibility) with the decoder on out-of-distribution c... | ok | {
"0521e67c6fe0f248": "IDEA:\nThe core research idea is to achieve identifiability in nonlinear Independent Component Analysis (ICA) by constraining the mixing function to classes characterized by specific *local geometric rigidity properties* of their derivatives, rather than relying solely on auxiliary variables or... | IDEA:
The core research idea is to achieve identifiability of a nonlinear compositional generative process by observing its *invariant differential structure* under known compositional transformations in the latent space, thereby enabling provable zero-shot generalization to novel compositions.
PROBLEM GAP:
Prior work... |
7Ttk3RzDeu | BooookScore: A systematic exploration of book-length summarization in the era of LLMs | data/extract_demo/7Ttk3RzDeu/paper.pdf | 36 | 18 | [
{
"authors": [
"Arman Cohan",
"Franck Dernoncourt",
"Doo Soon Kim",
"Trung Bui",
"Seokhwan Kim",
"Walter Chang",
"Nazli Goharian"
],
"doi": "10.18653/v1/n18-2097",
"ref_id": "b3",
"title": "A Discourse-Aware Attention Model for Abstractive Summarization ... | [
{
"authors": [
"Griffin Adams",
"Alex Fabbri",
"Faisal Ladhak",
"Eric Lehman",
"Noémie Elhadad"
],
"doi": "10.18653/v1/2023.newsum-1.7",
"ref_id": "b0",
"title": "From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting",
"year": "2023"
},
{... | [
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"af55e51... | IDEA:
The coherence of machine-generated summaries for documents exceeding typical language model context windows can be systematically and automatically evaluated by identifying a fine-grained taxonomy of specific coherence error types within the summary itself, without requiring a human-written reference summary or a... | ok | {
"0521e67c6fe0f248": null,
"0b86d375a5d13f12": "IDEA:\nThe core research idea is that summary quality, specifically the balance between informativeness and readability, can be systematically explored and controlled by iteratively increasing the information density (e.g., entity count) within a fixed summary length... | IDEA:
The core research idea is to redefine summarization as an interactive, goal-oriented dialogue between a user and an LLM, where the user provides fine-grained, task-specific feedback to iteratively refine a summary until it optimally serves their evolving information needs or decision-making process.
PROBLEM GAP:... |
3SJE1WLB4M | Generalization error of spectral algorithms | data/extract_demo/3SJE1WLB4M/paper.pdf | 30 | 0 | [] | [{"authors":["Frank Bauer","Sergei Pereverzev","Lorenzo Rosasco"],"doi":"10.1016/j.jco.2006.07.001",(...TRUNCATED) | [] | ["8274d51383c03b53","bbe715b2cd09b9fe","ee247113e41b2efd","7621c6fa2416601a","9deac7f6370cb9d3","a3d(...TRUNCATED) | "IDEA:\nThe paper introduces a unified framework for analyzing the generalization error of kernel le(...TRUNCATED) | ok | {"0521e67c6fe0f248":null,"0b86d375a5d13f12":null,"0f457873ec41d01a":null,"12c16108f2df7cf2":null,"33(...TRUNCATED) | "IDEA:\nDevelop computational frameworks that actively search for and characterize system configurat(...TRUNCATED) |
5ES5Hdlbxw | The Effective Horizon Explains Deep RL Performance in Stochastic Environments | data/extract_demo/5ES5Hdlbxw/paper.pdf | 34 | 23 | [{"authors":["Dimitri P Bertsekas","Maryam Kamgarpour"],"doi":"10.1109/mcs.2025.3615016","ref_id":"b(...TRUNCATED) | [{"authors":["András Antos","Csaba Szepesvári","Rémi Munos"],"doi":"","ref_id":"b0","title":"Fitt(...TRUNCATED) | ["64fbc35907476f08","d247d32f0d5cd297","f861c218b4b2d4f8","461b333b09a17365","95c33ce76502c327","30a(...TRUNCATED) | ["621ab31fb13cd70d","0c6c7114e57ea9a1","004ea6da2a333a07","5c11e8e8854b787c","79f7b60732f85ea8","64f(...TRUNCATED) | "IDEA:\nThe core idea is that the practical success of deep reinforcement learning, even with random(...TRUNCATED) | ok | {"0521e67c6fe0f248":null,"0b86d375a5d13f12":null,"0f457873ec41d01a":null,"12c16108f2df7cf2":null,"33(...TRUNCATED) | "IDEA:\nPropose a new theoretical framework and complexity measure, the \"Modality Switching Cost,\"(...TRUNCATED) |
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