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Open problems in Frontier LLMs

Efficient and accurate fact-checking of long, multi-claim texts1 paper raises it · 1 addresses it
Frontier LLMs are slow and expensive for verifying complex, multi-premise statements in reviews or papers.
- PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviewsraises · addresses
Adaptive reasoning depth for decision models2 papers raise it · 2 address it
Fixed-depth decision models waste computation on easy questions and underperform on hard ones.
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
- Jeva: System One Models from First Principlesraises · addresses
Structural requirements for decision-native models1 paper raises it · 3 address it
Current decision models lack null-awareness, permutation invariance, option scalability, and cacheability.
- Jeva: System One Models from First Principlesraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsaddresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsaddresses
Reliable no-thinking compliance in LLMs1 paper raises it · 1 addresses it
LLMs often ignore or misinterpret no-thinking instructions, leading to unwanted reasoning traces.
- Thinking Inertia: LLMs Keep Thinking When Told Not Toraises · addresses
Sustaining self-evolution in reasoning models1 paper raises it · 1 addresses it
Self-generated training data degrades over rounds due to invalid or repetitive questions.
- Questioning the Questions: Sustaining Self-Evolution in Reasoning Modelsraises · addresses

Named by an AI from these papers' abstracts.

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Recently funded

Topics in this field

reasoning traces · reasoning models · latent reasoning395 papers69 this week
dense retrieval · retrieval augmented generation · retrieval267 papers59 this week
attention heads · large language models · language models234 papers49 this week
question answering · answering · large language models147 papers46 this week
large language models · attribution · language models llms210 papers45 this week
large language models · large language · language models189 papers40 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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Recent papers

All papers in Research

arXiv cs.LGOct 13
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling
Jules Désir, Vincent Auriau, Martin Možina, Emmanuel Malherbe
Brick-and-mortar retailers face many different challenges that involve understanding thoroughly its products catalog and customer preferences. In particular, assortment optimization - proposing the ideal mix of products - and promotion planning hold a pivotal role in their strategy. By leveraging sales data, retailers can make informed decisions on which products to sell and how to manage inventory, based on customer preferences as well as regional and seasonal trends. It is especially crucial to capture interactions between products, in order to minimize the number of items that cannibalize each other's sales and to ensure that complementary products, which are often purchased together, are conjointly available and sold across all stores. In this paper, we propose a model of shopping basket that learns embeddings to represent interactions between products, prices and stores. Our model is built to uncover sales patterns from very large transaction datasets. In particular, the optimization loss is computed with random negative samples in order to overcome the computational bottlenecks that arise with large number of items. Our experiments on synthetic data show the efficiency of…
PDF arXiv
arXiv cs.AIOct 13
PaceReasoner: Label-Free Learning to Answer Briefly from Long Reasoning
Changguo Jia, Zhiyou Xiao, Xinbei Ma, Geng Chen, Weiming Zhang +3
Extended chain-of-thought reasoning improves problem-solving performance but increases inference cost and latency, motivating more accurate answers with shorter reasoning. We introduce PaceReasoner, a label-free test-time reinforcement learning framework that uses a model's LONG responses to improve its SHORT-mode accuracy. Both modes share a trainable policy, creating a need for reliable supervision, sufficient SHORT training samples, and continued LONG responses to guide learning. PaceReasoner combines all-response and LONG-only answer consensus through progressive, confidence-weighted supervision. A behavior reward gradually shifts from encouraging SHORT exploration to rewarding agreement with LONG answers, while a ratio controller regulates the balance between modes. Across three model backbones and four benchmarks, PaceReasoner improves average SHORT accuracy over the baselines by 7.4--13.5 percentage points, with fewer than 25 reasoning tokens per response on average. Ablations support the key components, and cross-dataset results demonstrate transfer beyond the adaptation set.
PDF arXiv
arXiv cs.LGOct 13
Evaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features
Samuel Xie, Noura Al-Moubayed
Sparse autoencoders decompose language model representations into interpretable features, but feature interpretation in this literature remains predominantly correlational. Attribution methods offer more direct estimates of feature influence, yet their faithfulness to actual causal effects has not been systematically evaluated in the SAE setting. We apply SHAP-based attribution to SAE latent features and evaluate it against direct causal intervention, using sentiment classification on SST-2 with GPT-2 Small and a 32,768-feature layer-7 SAE as a testbed. We compare SHAP to Integrated Gradients, Gradient$\times$Input, and probe-weight baselines across linear and MLP probe arms, scoring each against feature ablation and activation steering measured through the language model's own logit difference. Most faithfulness associations we measure do not survive multiple-comparison correction, and attribution methods that agree strongly with one another ($ρ= 0.42$--$0.80$) agree poorly with measured causal effects, indicating that inter-method agreement is not evidence of causal faithfulness. Two effects hold under control. First, local per-example attribution retains a directional…
PDF arXiv
arXiv cs.CLOct 13
Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages
Sophia Conrad, Fatma-Zohra Rezkellah, Christina Niklaus, Reto Gubelmann
We present Swiss4Arg, a multilingual dataset for argument mining based on official Swiss voting brochures, covering all four national languages: German, French, Italian, and Romansh (a Romance minority language exclusively spoken in Switzerland). The brochures contain explicitly formulated arguments systematically organized into pro and con sections, resulting in a cross-lingually aligned resource with balanced argumentative stance. We create a dataset that comprises 53 federal voting issues collected between 2019 and 2025, annotated for argument components (major claims, claims, premises, and non-argumentative units) and relations (support and attack) by three native speakers per language. Inter-annotator agreement (Krippendorff's alpha) reaches 0.548 for component classification and 0.623 for relation and target classification, computed jointly across all annotators on the parallel data. We release both adjudicated labels and individual annotator annotations, enabling analyses of disagreement and cross-linguistic variation, and provide baseline results using GPT-5.4 and SwissBERT. Swiss4Arg establishes a novel benchmark for multilingual argument mining, with a particular focus…
PDF arXiv
arXiv cs.IROct 13
FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval
Tripti Shukla, Shwetha Somasundaram, Akshat Gupta, Rohit Garg, Paul Duff +1
Evaluating fashion retrieval systems remains challenging due to the limited availability of benchmarks that capture the fine-grained, open-ended queries encountered in real-world search and recommendation. Existing benchmarks often rely on manual curation, predefined taxonomies, or synthetic prompts, limiting scalability and semantic coverage. We introduce FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval, an automated framework that derives user-type queries and relevance sets directly from catalog structure. Our key insight is that hierarchical discrete representations learned by Residual Quantized Variational Autoencoders (RQ-VAE) organize products into semantically coherent clusters at progressively finer granularities. We leverage this structure to construct FASHION-RQBENCH, a scalable benchmark without manual query annotation or predefined taxonomies. We propose four complementary tasks for evaluating ranking, relevance classification, fine-grained sibling discrimination, and paraphrase robustness, alongside query- and attribute-level relevance metrics. Experiments across recent retrieval models demonstrate that FASHION-RQBENCH…
PDF arXiv
arXiv cs.AIOct 13
Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents
Haochen Zhao, Minsheng Hao, Zheng Li, Yifan Li, Zikang Liu +6
Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back to retrieved evidence. Recent methods track citations to enforce grounding, but citation support alone does not establish source independence or whether an answer appropriately handles conflicting evidence. We argue that credibility in open-web retrieval depends on principled cross-source evidence grounding that accounts for source independence and systematically attributes and adjudicates conflicting evidence, while hierarchical judgments of these grounding decisions provide a unified signal for training and evaluation. To this end, we propose an evaluation framework, Cross-Source Evidence Grounding (CSEG), that employs a fine-grained, six-layer decision tree over the complete search trajectory. We introduce CSEG-Bench and trainable reward models to provide targeted supervision following this framework. Our approach helps eliminating the severe data skew of natural trajectories by providing targeted coverage over complex, deep-layer decision paths, such as cross-source conflict resolution and nuanced attribution, that standard…
PDF arXiv

Papers

Top tools here

All tools

NVIDIA Dynamo+62% downloads · 16k/wk+62.3%
Agno5 releases this week
FastMCP6 releases this week
BentoML+40% downloads · 44.6k/wk+39.6%
Pydantic AI+17% downloads · 1.5M/wk+17.0%

Topics in this field

reasoning traces · reasoning models · latent reasoning395 papers69 this week
dense retrieval · retrieval augmented generation · retrieval267 papers59 this week
attention heads · large language models · language models234 papers49 this week
question answering · answering · large language models147 papers46 this week
large language models · attribution · language models llms210 papers45 this week
large language models · large language · language models189 papers40 this week

Active contributors · 90 days

Recently funded

Market

Electric PlantSeed · Oct 20$11.2M
PennantSeed · Oct 20$5M
InfinigenceSeries B+ · Oct 12$343.6M
Phinity LabsSeed · Oct 12$5.2M
Lean SuperIntelligencePre-seed · Oct 9undisclosed

Recent papers

All papers in Research

arXiv cs.LGOct 13
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling
Jules Désir, Vincent Auriau, Martin Možina, Emmanuel Malherbe
Brick-and-mortar retailers face many different challenges that involve understanding thoroughly its products catalog and customer preferences. In particular, assortment optimization - proposing the ideal mix of products - and promotion planning hold a pivotal role in their strategy. By leveraging sales data, retailers can make informed decisions on which products to sell and how to manage inventory, based on customer preferences as well as regional and seasonal trends. It is especially crucial to capture interactions between products, in order to minimize the number of items that cannibalize each other's sales and to ensure that complementary products, which are often purchased together, are conjointly available and sold across all stores. In this paper, we propose a model of shopping basket that learns embeddings to represent interactions between products, prices and stores. Our model is built to uncover sales patterns from very large transaction datasets. In particular, the optimization loss is computed with random negative samples in order to overcome the computational bottlenecks that arise with large number of items. Our experiments on synthetic data show the efficiency of…
PDF arXiv
arXiv cs.AIOct 13
PaceReasoner: Label-Free Learning to Answer Briefly from Long Reasoning
Changguo Jia, Zhiyou Xiao, Xinbei Ma, Geng Chen, Weiming Zhang +3
Extended chain-of-thought reasoning improves problem-solving performance but increases inference cost and latency, motivating more accurate answers with shorter reasoning. We introduce PaceReasoner, a label-free test-time reinforcement learning framework that uses a model's LONG responses to improve its SHORT-mode accuracy. Both modes share a trainable policy, creating a need for reliable supervision, sufficient SHORT training samples, and continued LONG responses to guide learning. PaceReasoner combines all-response and LONG-only answer consensus through progressive, confidence-weighted supervision. A behavior reward gradually shifts from encouraging SHORT exploration to rewarding agreement with LONG answers, while a ratio controller regulates the balance between modes. Across three model backbones and four benchmarks, PaceReasoner improves average SHORT accuracy over the baselines by 7.4--13.5 percentage points, with fewer than 25 reasoning tokens per response on average. Ablations support the key components, and cross-dataset results demonstrate transfer beyond the adaptation set.
PDF arXiv
arXiv cs.LGOct 13
Evaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features
Samuel Xie, Noura Al-Moubayed
Sparse autoencoders decompose language model representations into interpretable features, but feature interpretation in this literature remains predominantly correlational. Attribution methods offer more direct estimates of feature influence, yet their faithfulness to actual causal effects has not been systematically evaluated in the SAE setting. We apply SHAP-based attribution to SAE latent features and evaluate it against direct causal intervention, using sentiment classification on SST-2 with GPT-2 Small and a 32,768-feature layer-7 SAE as a testbed. We compare SHAP to Integrated Gradients, Gradient$\times$Input, and probe-weight baselines across linear and MLP probe arms, scoring each against feature ablation and activation steering measured through the language model's own logit difference. Most faithfulness associations we measure do not survive multiple-comparison correction, and attribution methods that agree strongly with one another ($ρ= 0.42$--$0.80$) agree poorly with measured causal effects, indicating that inter-method agreement is not evidence of causal faithfulness. Two effects hold under control. First, local per-example attribution retains a directional…
PDF arXiv
arXiv cs.CLOct 13
Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages
Sophia Conrad, Fatma-Zohra Rezkellah, Christina Niklaus, Reto Gubelmann
We present Swiss4Arg, a multilingual dataset for argument mining based on official Swiss voting brochures, covering all four national languages: German, French, Italian, and Romansh (a Romance minority language exclusively spoken in Switzerland). The brochures contain explicitly formulated arguments systematically organized into pro and con sections, resulting in a cross-lingually aligned resource with balanced argumentative stance. We create a dataset that comprises 53 federal voting issues collected between 2019 and 2025, annotated for argument components (major claims, claims, premises, and non-argumentative units) and relations (support and attack) by three native speakers per language. Inter-annotator agreement (Krippendorff's alpha) reaches 0.548 for component classification and 0.623 for relation and target classification, computed jointly across all annotators on the parallel data. We release both adjudicated labels and individual annotator annotations, enabling analyses of disagreement and cross-linguistic variation, and provide baseline results using GPT-5.4 and SwissBERT. Swiss4Arg establishes a novel benchmark for multilingual argument mining, with a particular focus…
PDF arXiv
arXiv cs.IROct 13
FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval
Tripti Shukla, Shwetha Somasundaram, Akshat Gupta, Rohit Garg, Paul Duff +1
Evaluating fashion retrieval systems remains challenging due to the limited availability of benchmarks that capture the fine-grained, open-ended queries encountered in real-world search and recommendation. Existing benchmarks often rely on manual curation, predefined taxonomies, or synthetic prompts, limiting scalability and semantic coverage. We introduce FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval, an automated framework that derives user-type queries and relevance sets directly from catalog structure. Our key insight is that hierarchical discrete representations learned by Residual Quantized Variational Autoencoders (RQ-VAE) organize products into semantically coherent clusters at progressively finer granularities. We leverage this structure to construct FASHION-RQBENCH, a scalable benchmark without manual query annotation or predefined taxonomies. We propose four complementary tasks for evaluating ranking, relevance classification, fine-grained sibling discrimination, and paraphrase robustness, alongside query- and attribute-level relevance metrics. Experiments across recent retrieval models demonstrate that FASHION-RQBENCH…
PDF arXiv
arXiv cs.AIOct 13
Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents
Haochen Zhao, Minsheng Hao, Zheng Li, Yifan Li, Zikang Liu +6
Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back to retrieved evidence. Recent methods track citations to enforce grounding, but citation support alone does not establish source independence or whether an answer appropriately handles conflicting evidence. We argue that credibility in open-web retrieval depends on principled cross-source evidence grounding that accounts for source independence and systematically attributes and adjudicates conflicting evidence, while hierarchical judgments of these grounding decisions provide a unified signal for training and evaluation. To this end, we propose an evaluation framework, Cross-Source Evidence Grounding (CSEG), that employs a fine-grained, six-layer decision tree over the complete search trajectory. We introduce CSEG-Bench and trainable reward models to provide targeted supervision following this framework. Our approach helps eliminating the severe data skew of natural trajectories by providing targeted coverage over complex, deep-layer decision paths, such as cross-source conflict resolution and nuanced attribution, that standard…
PDF arXiv

Activity · 12 weeks

Open problems in Frontier LLMs

Efficient and accurate fact-checking of long, multi-claim texts1 paper raises it · 1 addresses it
Frontier LLMs are slow and expensive for verifying complex, multi-premise statements in reviews or papers.
- PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviewsraises · addresses
Adaptive reasoning depth for decision models2 papers raise it · 2 address it
Fixed-depth decision models waste computation on easy questions and underperform on hard ones.
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
- Jeva: System One Models from First Principlesraises · addresses
Structural requirements for decision-native models1 paper raises it · 3 address it
Current decision models lack null-awareness, permutation invariance, option scalability, and cacheability.
- Jeva: System One Models from First Principlesraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsaddresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsaddresses
Reliable no-thinking compliance in LLMs1 paper raises it · 1 addresses it
LLMs often ignore or misinterpret no-thinking instructions, leading to unwanted reasoning traces.
- Thinking Inertia: LLMs Keep Thinking When Told Not Toraises · addresses
Sustaining self-evolution in reasoning models1 paper raises it · 1 addresses it
Self-generated training data degrades over rounds due to invalid or repetitive questions.
- Questioning the Questions: Sustaining Self-Evolution in Reasoning Modelsraises · addresses

Named by an AI from these papers' abstracts.

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