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Open problems in Frontier LLMs

Structural requirements for decision-native models1 paper raises it · 1 addresses it
Current decision models often ignore key properties like null-awareness, permutation invariance, and option scalability, limiting their reliability and efficiency.
- Jeva: System One Models from First Principlesraises · addresses
Efficient and calibrated decision-making for search agents1 paper raises it · 1 addresses it
Generative models introduce latency and unreliable confidence in short, repeated decisions, hindering real-time search agent performance.
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsraises · addresses
Sustaining self-evolution in reasoning models1 paper raises it · 1 addresses it
Self-training on generated questions can collapse performance due to invalid or repetitive questions, limiting long-term improvement.
- Questioning the Questions: Sustaining Self-Evolution in Reasoning Modelsraises · addresses
Memory consolidation in context-conditioned models1 paper raises it · 1 addresses it
Gains from context are often lost after its removal, reducing the long-term utility of contextual learning.
- Cortex: Consolidating Context into Parametric Memoryraises · addresses
Detecting and mitigating scientific slop in AI-generated papers1 paper raises it · 1 addresses it
AI-generated scientific papers can appear plausible but contain flawed reasoning, misleading readers and degrading research quality.
- Science or Slop?: Benchmarking and Mitigating Scientific Slop in AI-Generated Papersraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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Recently funded

Topics in this field

reasoning traces · reasoning models · latent reasoning381 papers58 this week
dense retrieval · retrieval augmented generation · retrieval256 papers50 this week
attention heads · large language models · language models231 papers48 this week
question answering · answering · large language models140 papers40 this week
large language models · attribution · language models llms203 papers38 this week
large language models · large language · language models184 papers35 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.CLOct 15
System1-Arena: Which Decisions Can Be Left to System 1?
Tianyu Chen, Xinyang Liu, Ruyi An, Zhendong Wang, Mingyuan Zhou
Large language models (LLMs) often make discrete decisions, such as flagging phishing or escalating to a human. System-1 models, such as Jev and the OpenAI Decisions API, return probabilities over options in a single forward pass, while System-2 approaches generate reasoning steps. Which decisions can System-1 models handle, and which require generative reasoning? We introduce System1-Arena, a benchmark that gives all models the same decision requests and scores their probabilities. It contains 92k questions across 105 tasks from 268 public sources, in four groups: text classification and knowledge, applied decisions, evaluation and control, and agent and action. It also includes 138 closed-loop environments, from web shopping and SWE-bench coding control to robotics, games, and driving; in real-time mode, the world evolves while a model decides. We evaluate 30 models: 7 closed LLMs, 13 open LLMs read at the answer token, and 10 System-1 models. The largest System-1 models match or exceed fast closed LLMs in non-agentic accuracy, with better-calibrated probabilities at one-quarter of the latency. A reasoning LLM is the most accurate, at about 70x the cost of a fast LLM. In closed…
PDF arXiv
arXiv cs.LGOct 15
Representation Redistribution in Sparse Autoencoders
Ziye Mei, Haotian Ma
Sparse autoencoders (SAEs) decompose language model activations into sparse features, but prompt transformations designed to preserve the underlying request can redistribute activity across them. A feature change observed on one transformed prompt need not recur, and even a recurring change need not affect a downstream computation. We introduce the redistribution sensitivity score (RSS), which identifies large feature responses that recur in a consistent direction, and evaluate the resulting supports on variants excluded from selection. Across 27 pretrained SAEs spanning eight language models, the tested prompt transformations produce a reproducible redistribution profile on these excluded variants. We then separate recurring representation change from signed readout contribution and decision failure. For a fixed linear readout, an exact decomposition shows how response magnitude, readout coefficients, directional alignment, and the clean margin determine downstream consequences. In two frozen safety readouts, combining RSS support erosion with the full clean margin yields recurrent failure AUROCs of 0.935 and 0.958 using median supports of 20 and 19 features, compared with 0.965…
PDF arXiv
arXiv cs.CLOct 15
KBS: A Korean Benchmark Suite for Foundation Model Evaluation
Yeonkyoung So, Jongmin Kim, Sungmok Jung, Gyuseong Lee, Sangho Kim +11
We present a Korean Benchmark Suite (KBS) comprising Ko-ARC, Ko-GSM8K, Ko-EQ-Bench, Ko-WinoGrande, Ko-LAMBADA, and Ko-IFEval, covering six capabilities across 9,396 items. The suite combines review, localization, direct Korean construction, and redesign for Korean, avoiding a translation-only approach to benchmark adaptation. We evaluate Korean-developed models, multilingual open models, and closed API models under protocols that account for model access, prompt format, and scoring differences. Results show that Korean benchmark scores depend on task construction, scoring rules, prompting, and model post-training. These findings suggest that Korean evaluation should report both final scores and the settings used to obtain them.
PDF arXiv
arXiv cs.IROct 15
RecSoup: Efficient Model Merging Approach for Controllable Multi-Objective Recommendation
Zidong Jia, Guofu Xie, Xiao Zhang
Recommender systems often need to balance multiple conflicting objectives. Controllable Multi-Objective Recommendation (CMOR) enables dynamic adjustment of objective trade-offs at inference time without retraining, adapting to shifting user or system requirements. Existing CMOR approaches typically rely on output-level reranking or parameter-generating modules, which either limit the scope of feasible trade-offs or incur substantial training and inference cost. Model merging provides a promising alternative by reusing multiple objective-specific models via parameter combination. However, its direct application to recommender systems is challenging due to the lack of a shared linear regime and cross-objective interference. To address these issues, we propose RecSoup, a lightweight two-stage approach for effective CMOR via specialized model merging. RecSoup first mitigates parameter interference across objectives through a pruning strategy that jointly considers objective-specific importance and cross-objective relevance. It then performs adaptive parameter merging via a learnable preference-to-coefficient mapping, allowing dynamic trade-off control at inference time without joint…
PDF arXiv
arXiv cs.AIOct 15
Entertain, then Cancel: How Thinking Makes Reasoning Models Read Some and Or Literally
Zhizhuo Kou, Jian Yang, Pengfei Hao, Junyu Luo, Jingshu Peng +2
When a speaker says ``some students passed'', listeners infer that not all of them did. Reasoning models now let users decide how long a model thinks before it answers, but we do not know what extra thinking does to such everyday inferences. We study this directly: we keep a model's weights fixed, change only how much it thinks, and record which reading it chooses. A second tool cuts the model's own reasoning at successive points and asks for an answer, showing where the reading changes. Thinking makes models read logical words as logic: it cancels the implicatures of \emph{some} (\emph{not all}) and \emph{or} (\emph{not both}) in most tested open reasoning models and in GPT-5, while context-driven inferences such as indirect answers improve. The trace shows why: models first entertain the enriched reading and cancel it later. Answering from that early reading, an exploratory inference-time remedy, recovers these implicatures without losing literal accuracy. Evaluations of reasoning models should measure how they interpret, not only how accurate they are.
PDF arXiv
arXiv cs.CLOct 15
Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG
Jiaqi Wei, Wenxuan Huang, Chengdong Xu, Xiang Zhang, Wanli Ouyang
Multi-hop retrieval-augmented generation (RAG) requires an agent to retrieve evidence for a sequence of dependent sub-questions. When a wrong answer lacks a required passage, the passage may be inaccessible to the retriever, or the agent may have failed to issue a query that would retrieve it. Standard retrieval evaluations conflate these two causes. We study how much apparent retrieval failure is instead caused by search drift: the search no longer tracks an unanswered sub-question. Holding the retriever, corpus, and generation model fixed, we introduce ExecutionRAG, a training-free requirement register that records open sub-questions and their supporting evidence. The register changes search guidance but not evidence access, so each gold passage it recovers certifies that the passage was reachable by the original retriever. On 1,000 MuSiQue questions, the number of wrong answers with unretrieved required evidence falls from 382 to 234 with all-MiniLM-L6-v2 and from 383 to 274 with Qwen3-Embedding-0.6B. These counts correspond to 69% to 44% and 66% to 48% of each system's wrong answers, respectively. Crucially, evidence recovery does not produce a proportional reduction in total…
PDF arXiv

Papers

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All tools

NVIDIA Dynamo+60% downloads · 15.8k/wk+60.0%
Agno4 releases this week
FastMCP6 releases this week
BentoML+30% downloads · 41k/wk+29.7%
n8n+25% downloads · 148.3k/wk+25.0%

Topics in this field

reasoning traces · reasoning models · latent reasoning381 papers58 this week
dense retrieval · retrieval augmented generation · retrieval256 papers50 this week
attention heads · large language models · language models231 papers48 this week
question answering · answering · large language models140 papers40 this week
large language models · attribution · language models llms203 papers38 this week
large language models · large language · language models184 papers35 this week

Recently funded

Market

InfinigenceSeries B+ · Oct 12$343.6M
Lean SuperIntelligencePre-seed · Oct 9undisclosed
VinciSeries B+ · Oct 6$250M
Siena AISeries A · Oct 6$17M
Ocular AIPre-seed · Oct 5$2M

Active contributors · 90 days

Activity · 12 weeks

Fields · AI · updated 1m ago

Frontier LLMs

The largest general-purpose language models, their releases, capabilities, scaling and training at frontier scale.

421papers this week−13.7% vs last week

All papers · 2.3k With code · 54

Newest Most relevant

2.1k papers this month

Open problems in Frontier LLMs

Structural requirements for decision-native models1 paper raises it · 1 addresses it
Current decision models often ignore key properties like null-awareness, permutation invariance, and option scalability, limiting their reliability and efficiency.
- Jeva: System One Models from First Principlesraises · addresses
Efficient and calibrated decision-making for search agents1 paper raises it · 1 addresses it
Generative models introduce latency and unreliable confidence in short, repeated decisions, hindering real-time search agent performance.
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsraises · addresses
Sustaining self-evolution in reasoning models1 paper raises it · 1 addresses it
Self-training on generated questions can collapse performance due to invalid or repetitive questions, limiting long-term improvement.
- Questioning the Questions: Sustaining Self-Evolution in Reasoning Modelsraises · addresses
Memory consolidation in context-conditioned models1 paper raises it · 1 addresses it
Gains from context are often lost after its removal, reducing the long-term utility of contextual learning.
- Cortex: Consolidating Context into Parametric Memoryraises · addresses
Detecting and mitigating scientific slop in AI-generated papers1 paper raises it · 1 addresses it
AI-generated scientific papers can appear plausible but contain flawed reasoning, misleading readers and degrading research quality.
- Science or Slop?: Benchmarking and Mitigating Scientific Slop in AI-Generated Papersraises · addresses

Named by an AI from these papers' abstracts.

Recent papers

All papers in Research

arXiv cs.CLOct 15
System1-Arena: Which Decisions Can Be Left to System 1?
Tianyu Chen, Xinyang Liu, Ruyi An, Zhendong Wang, Mingyuan Zhou
Large language models (LLMs) often make discrete decisions, such as flagging phishing or escalating to a human. System-1 models, such as Jev and the OpenAI Decisions API, return probabilities over options in a single forward pass, while System-2 approaches generate reasoning steps. Which decisions can System-1 models handle, and which require generative reasoning? We introduce System1-Arena, a benchmark that gives all models the same decision requests and scores their probabilities. It contains 92k questions across 105 tasks from 268 public sources, in four groups: text classification and knowledge, applied decisions, evaluation and control, and agent and action. It also includes 138 closed-loop environments, from web shopping and SWE-bench coding control to robotics, games, and driving; in real-time mode, the world evolves while a model decides. We evaluate 30 models: 7 closed LLMs, 13 open LLMs read at the answer token, and 10 System-1 models. The largest System-1 models match or exceed fast closed LLMs in non-agentic accuracy, with better-calibrated probabilities at one-quarter of the latency. A reasoning LLM is the most accurate, at about 70x the cost of a fast LLM. In closed…
PDF arXiv
arXiv cs.LGOct 15
Representation Redistribution in Sparse Autoencoders
Ziye Mei, Haotian Ma
Sparse autoencoders (SAEs) decompose language model activations into sparse features, but prompt transformations designed to preserve the underlying request can redistribute activity across them. A feature change observed on one transformed prompt need not recur, and even a recurring change need not affect a downstream computation. We introduce the redistribution sensitivity score (RSS), which identifies large feature responses that recur in a consistent direction, and evaluate the resulting supports on variants excluded from selection. Across 27 pretrained SAEs spanning eight language models, the tested prompt transformations produce a reproducible redistribution profile on these excluded variants. We then separate recurring representation change from signed readout contribution and decision failure. For a fixed linear readout, an exact decomposition shows how response magnitude, readout coefficients, directional alignment, and the clean margin determine downstream consequences. In two frozen safety readouts, combining RSS support erosion with the full clean margin yields recurrent failure AUROCs of 0.935 and 0.958 using median supports of 20 and 19 features, compared with 0.965…
PDF arXiv
arXiv cs.CLOct 15
KBS: A Korean Benchmark Suite for Foundation Model Evaluation
Yeonkyoung So, Jongmin Kim, Sungmok Jung, Gyuseong Lee, Sangho Kim +11
We present a Korean Benchmark Suite (KBS) comprising Ko-ARC, Ko-GSM8K, Ko-EQ-Bench, Ko-WinoGrande, Ko-LAMBADA, and Ko-IFEval, covering six capabilities across 9,396 items. The suite combines review, localization, direct Korean construction, and redesign for Korean, avoiding a translation-only approach to benchmark adaptation. We evaluate Korean-developed models, multilingual open models, and closed API models under protocols that account for model access, prompt format, and scoring differences. Results show that Korean benchmark scores depend on task construction, scoring rules, prompting, and model post-training. These findings suggest that Korean evaluation should report both final scores and the settings used to obtain them.
PDF arXiv
arXiv cs.IROct 15
RecSoup: Efficient Model Merging Approach for Controllable Multi-Objective Recommendation
Zidong Jia, Guofu Xie, Xiao Zhang
Recommender systems often need to balance multiple conflicting objectives. Controllable Multi-Objective Recommendation (CMOR) enables dynamic adjustment of objective trade-offs at inference time without retraining, adapting to shifting user or system requirements. Existing CMOR approaches typically rely on output-level reranking or parameter-generating modules, which either limit the scope of feasible trade-offs or incur substantial training and inference cost. Model merging provides a promising alternative by reusing multiple objective-specific models via parameter combination. However, its direct application to recommender systems is challenging due to the lack of a shared linear regime and cross-objective interference. To address these issues, we propose RecSoup, a lightweight two-stage approach for effective CMOR via specialized model merging. RecSoup first mitigates parameter interference across objectives through a pruning strategy that jointly considers objective-specific importance and cross-objective relevance. It then performs adaptive parameter merging via a learnable preference-to-coefficient mapping, allowing dynamic trade-off control at inference time without joint…
PDF arXiv
arXiv cs.AIOct 15
Entertain, then Cancel: How Thinking Makes Reasoning Models Read Some and Or Literally
Zhizhuo Kou, Jian Yang, Pengfei Hao, Junyu Luo, Jingshu Peng +2
When a speaker says ``some students passed'', listeners infer that not all of them did. Reasoning models now let users decide how long a model thinks before it answers, but we do not know what extra thinking does to such everyday inferences. We study this directly: we keep a model's weights fixed, change only how much it thinks, and record which reading it chooses. A second tool cuts the model's own reasoning at successive points and asks for an answer, showing where the reading changes. Thinking makes models read logical words as logic: it cancels the implicatures of \emph{some} (\emph{not all}) and \emph{or} (\emph{not both}) in most tested open reasoning models and in GPT-5, while context-driven inferences such as indirect answers improve. The trace shows why: models first entertain the enriched reading and cancel it later. Answering from that early reading, an exploratory inference-time remedy, recovers these implicatures without losing literal accuracy. Evaluations of reasoning models should measure how they interpret, not only how accurate they are.
PDF arXiv
arXiv cs.CLOct 15
Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG
Jiaqi Wei, Wenxuan Huang, Chengdong Xu, Xiang Zhang, Wanli Ouyang
Multi-hop retrieval-augmented generation (RAG) requires an agent to retrieve evidence for a sequence of dependent sub-questions. When a wrong answer lacks a required passage, the passage may be inaccessible to the retriever, or the agent may have failed to issue a query that would retrieve it. Standard retrieval evaluations conflate these two causes. We study how much apparent retrieval failure is instead caused by search drift: the search no longer tracks an unanswered sub-question. Holding the retriever, corpus, and generation model fixed, we introduce ExecutionRAG, a training-free requirement register that records open sub-questions and their supporting evidence. The register changes search guidance but not evidence access, so each gold passage it recovers certifies that the passage was reachable by the original retriever. On 1,000 MuSiQue questions, the number of wrong answers with unretrieved required evidence falls from 382 to 234 with all-MiniLM-L6-v2 and from 383 to 274 with Qwen3-Embedding-0.6B. These counts correspond to 69% to 44% and 66% to 48% of each system's wrong answers, respectively. Crucially, evidence recovery does not produce a proportional reduction in total…
PDF arXiv