Spotlight

Papers

Field

Top tools here

Open problems in Classifier & causal discovery

Lack of standardized benchmarks for agentic black-box optimization1 paper raises it · 1 addresses it
Makes it hard to compare methods and isolate the effects of design choices across domains.
- A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimizationraises · addresses
Identifiability of individual world latents without reconstruction1 paper raises it · 1 addresses it
Current methods often rely on reconstruction or auxiliary supervision, limiting their applicability.
- DSReg: Provably Recovering Individual World Latents without Reconstructionraises · addresses
Handling noisy complementary labels in classification1 paper raises it · 1 addresses it
Complementary labels can be cheap but are prone to errors that harm model training.
- CAVA: Complementary-Label-Aware Data Valuationraises · addresses
Calibration in class-incremental learning1 paper raises it · 1 addresses it
Confidence calibration degrades asymmetrically across tasks as models adapt to new data.
- MT-CC: Multi-Group Temperature Scaling for Asymmetric Calibration Behavior in Class-Incremental Learningraises · addresses
Training with ML-labeled data under label noise1 paper raises it · 1 addresses it
ML-labeled data can introduce errors that degrade model performance if not addressed.
- Training with ML-labeled Data via Stochastic First- and Zeroth-Order Optimizationraises · addresses

Named by an AI from these papers' abstracts.

Open problems

Recently funded

Topics in this field

classifier · supervised · machine learning175 papers36 this week
causal discovery · causal inference · causal models118 papers15 this week
bayesian optimization · bayesian optimization bo · black box optimization91 papers12 this week
conformal prediction · split conformal prediction · aware conformal67 papers11 this week

Topics in this field

Active contributors · 90 days

Yujia ZhengDSReg: Provably Recovering Individual World Latents without Reconstruction4
Gunwoong ParkLocal Fisher Information Enables Sparse Causal Discovery4
Isabel ValeraDeDiCa: A Scalable Deconfounding Discrete Causal Generative Model3
Eyke HüllermeierUncertainty Quantification in Pairwise Difference Learning for Classification3
Yuheng JiaCalibrated Disambiguation for Partial Multi-label Learning3
Sébastien DesterckeInterval-valued SHAP in Tree-Based Models3

Active contributors

Activity · 12 weeks

Coming with more sources: repos

Activity

★ Spotlight this month

arXiv cs.AIOct 15

Are You Sure Your LLM Is Sure? Repairing and Auditing Confidence

Arijit Dey, Bhartendu Kumar, Ramya Hebbalaguppe, Khuushi Maheshwari

Confidence scores determine whether an LLM answer is accepted, reviewed, or regenerated, yet scalar recalibration can only correct errors visible through the score itself. We introduce RADAR (Repair, Adopt, and Diagnose After Recalibration), a reliability framework that separates confidence repair from deciding whether to deploy the repair and auditing the errors that remain. Under squared loss, we decompose prediction risk into outcome noise, information hidden by the score, and mapping error; restricting repair to order-preserving maps additionally introduces a pooling gap. For repair, we propose Probe-Anchored Isotonic Calibration (PAIC), which learns a monotone map from correctness-probe predictions on unlabeled target prompts. PAIC's excess risk depends on the probe's score-conditional bias and fitting error rather than its overall accuracy. For adoption, we derive a paired procedure with finite-sample control of false improvements. For auditing, we develop a prompt-clustered kernel test that detects feature-aligned residual errors while accounting for dependent answers from the same prompt. Across multiple LLM checkpoints, benchmarks, and distribution shifts, richer…

PDF arXiv page Details

Spotlight

Recent papers

All in Research →

arXiv cs.LGCodeOct 8▲ 14
A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization
Ming Chen, Rong-Xi Tan, Ke Xue, Yu-Jie Zhou, Taiye Lu +6
Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations are expensive and limited. Recent large language model (LLM) agents offer a new way to approach BBO by combining task semantics, computation, optimization tools, and feedback-driven decision making, showing great potential due to the integration with mathematically rigorous tools. However, existing agentic BBO studies use different task domains and system configurations, making their results difficult to compare and the effects of individual design choices hard to isolate. We therefore introduce AgenticBBO-Bench, a cross-domain benchmark for agentic BBO spanning synthetic functions, hyperparameter optimization, database tuning, chip design, and molecular design under a unified finite-budget evaluation protocol. In our experiments, agentic BBO achieves higher family-averaged scores than direct LLM-based methods in all five domains and outperforms the best numerical optimizers in four. We further study three factors shaping agent performance: optimization tools, task information and prior knowledge, and the role of the LLM during search. Our results show that additional…
PDF arXiv Code
arXiv cs.LGCodeOct 7▲ 3
DSReg: Provably Recovering Individual World Latents without Reconstruction
Yujia Zheng, David Klindt, Randall Balestriero, Bernhard Schölkopf
Methods that recover individual latent variables of the world, from nonlinear ICA to dictionary learning and causal representation learning, anchor the latents to observations through reconstruction, auxiliary supervision, or distributional asymmetries such as non-Gaussianity. Methods without these anchors, including joint-embedding predictive architectures (JEPAs), identify the latent state only up to a linear transformation, so individual latents remain mixed. We close this gap: individual world latents can be provably recovered with no reconstruction, no decoder, and no labels. The key condition is Structural Diversity: different latents leave distinct dependency footprints on observations, just as no two snowflakes are alike. Building on the linear identifiability that LeJEPA provides, we prove that under Structural Diversity, DSReg (Dependency-Sparsity Regularization) recovers individual world latents up to signed permutation, without reconstruction or a decoder. It applies post hoc to any linearly identified representation, reusing trained checkpoints at no loss over joint training, and establishes the first fully identifiable JEPA that recovers every world latent.…
PDF arXiv Code

Papers

Topics in this field

classifier · supervised · machine learning175 papers36 this week
causal discovery · causal inference · causal models118 papers15 this week
bayesian optimization · bayesian optimization bo · black box optimization91 papers12 this week
conformal prediction · split conformal prediction · aware conformal67 papers11 this week

Active contributors · 90 days

Yujia ZhengDSReg: Provably Recovering Individual World Latents without Reconstruction4
Gunwoong ParkLocal Fisher Information Enables Sparse Causal Discovery4
Isabel ValeraDeDiCa: A Scalable Deconfounding Discrete Causal Generative Model3
Eyke HüllermeierUncertainty Quantification in Pairwise Difference Learning for Classification3
Yuheng JiaCalibrated Disambiguation for Partial Multi-label Learning3
Sébastien DesterckeInterval-valued SHAP in Tree-Based Models3

★ Spotlight this month

arXiv cs.AIOct 15

Are You Sure Your LLM Is Sure? Repairing and Auditing Confidence

Arijit Dey, Bhartendu Kumar, Ramya Hebbalaguppe, Khuushi Maheshwari

Confidence scores determine whether an LLM answer is accepted, reviewed, or regenerated, yet scalar recalibration can only correct errors visible through the score itself. We introduce RADAR (Repair, Adopt, and Diagnose After Recalibration), a reliability framework that separates confidence repair from deciding whether to deploy the repair and auditing the errors that remain. Under squared loss, we decompose prediction risk into outcome noise, information hidden by the score, and mapping error; restricting repair to order-preserving maps additionally introduces a pooling gap. For repair, we propose Probe-Anchored Isotonic Calibration (PAIC), which learns a monotone map from correctness-probe predictions on unlabeled target prompts. PAIC's excess risk depends on the probe's score-conditional bias and fitting error rather than its overall accuracy. For adoption, we derive a paired procedure with finite-sample control of false improvements. For auditing, we develop a prompt-clustered kernel test that detects feature-aligned residual errors while accounting for dependent answers from the same prompt. Across multiple LLM checkpoints, benchmarks, and distribution shifts, richer…

PDF arXiv page Details

Activity · 12 weeks

Coming with more sources: repos

Open problems in Classifier & causal discovery

Lack of standardized benchmarks for agentic black-box optimization1 paper raises it · 1 addresses it
Makes it hard to compare methods and isolate the effects of design choices across domains.
- A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimizationraises · addresses
Identifiability of individual world latents without reconstruction1 paper raises it · 1 addresses it
Current methods often rely on reconstruction or auxiliary supervision, limiting their applicability.
- DSReg: Provably Recovering Individual World Latents without Reconstructionraises · addresses
Handling noisy complementary labels in classification1 paper raises it · 1 addresses it
Complementary labels can be cheap but are prone to errors that harm model training.
- CAVA: Complementary-Label-Aware Data Valuationraises · addresses
Calibration in class-incremental learning1 paper raises it · 1 addresses it
Confidence calibration degrades asymmetrically across tasks as models adapt to new data.
- MT-CC: Multi-Group Temperature Scaling for Asymmetric Calibration Behavior in Class-Incremental Learningraises · addresses
Training with ML-labeled data under label noise1 paper raises it · 1 addresses it
ML-labeled data can introduce errors that degrade model performance if not addressed.
- Training with ML-labeled Data via Stochastic First- and Zeroth-Order Optimizationraises · addresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 36m ago

Classifier & causal discovery

Found by the topic model: classifier, causal discovery, bayesian optimization.

74papers this week−32.7% vs last week

All papers · 451 With code · 2

Newest Most relevant

426 papers this month

Recent papers

All in Research →

arXiv cs.LGCodeOct 8▲ 14
A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization
Ming Chen, Rong-Xi Tan, Ke Xue, Yu-Jie Zhou, Taiye Lu +6
Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations are expensive and limited. Recent large language model (LLM) agents offer a new way to approach BBO by combining task semantics, computation, optimization tools, and feedback-driven decision making, showing great potential due to the integration with mathematically rigorous tools. However, existing agentic BBO studies use different task domains and system configurations, making their results difficult to compare and the effects of individual design choices hard to isolate. We therefore introduce AgenticBBO-Bench, a cross-domain benchmark for agentic BBO spanning synthetic functions, hyperparameter optimization, database tuning, chip design, and molecular design under a unified finite-budget evaluation protocol. In our experiments, agentic BBO achieves higher family-averaged scores than direct LLM-based methods in all five domains and outperforms the best numerical optimizers in four. We further study three factors shaping agent performance: optimization tools, task information and prior knowledge, and the role of the LLM during search. Our results show that additional…
PDF arXiv Code
arXiv cs.LGCodeOct 7▲ 3
DSReg: Provably Recovering Individual World Latents without Reconstruction
Yujia Zheng, David Klindt, Randall Balestriero, Bernhard Schölkopf
Methods that recover individual latent variables of the world, from nonlinear ICA to dictionary learning and causal representation learning, anchor the latents to observations through reconstruction, auxiliary supervision, or distributional asymmetries such as non-Gaussianity. Methods without these anchors, including joint-embedding predictive architectures (JEPAs), identify the latent state only up to a linear transformation, so individual latents remain mixed. We close this gap: individual world latents can be provably recovered with no reconstruction, no decoder, and no labels. The key condition is Structural Diversity: different latents leave distinct dependency footprints on observations, just as no two snowflakes are alike. Building on the linear identifiability that LeJEPA provides, we prove that under Structural Diversity, DSReg (Dependency-Sparsity Regularization) recovers individual world latents up to signed permutation, without reconstruction or a decoder. It applies post hoc to any linearly identified representation, reusing trained checkpoints at no loss over joint training, and establishes the first fully identifiable JEPA that recovers every world latent.…
PDF arXiv Code

Field

Spotlight

Papers

Field

Top tools here

Open problems in Classifier & causal discovery

Open problems

Recently funded

Topics in this field

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Are You Sure Your LLM Is Sure? Repairing and Auditing Confidence

Spotlight

Recent papers

A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization

DSReg: Provably Recovering Individual World Latents without Reconstruction

Papers

Topics in this field

Active contributors · 90 days

Are You Sure Your LLM Is Sure? Repairing and Auditing Confidence

Activity · 12 weeks

Open problems in Classifier & causal discovery

Classifier & causal discovery

Recent papers

A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization

DSReg: Provably Recovering Individual World Latents without Reconstruction