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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

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reasoning traces · reasoning models · latent reasoning381 papers75 this week
attention heads · large language models · language models231 papers55 this week
dense retrieval · retrieval augmented generation · retrieval256 papers53 this week
question answering · answering · large language models140 papers46 this week
large language models · attribution · language models llms203 papers43 this week
large language models · large language · language models184 papers40 this week

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arXiv cs.AICodeOct 2▲ 52
Multilingual GSM-Symbolic: What determines capability transfer across languages?
Kenneth Enevoldsen, Riley Herchert, Sofie Mosegaard, Dan Saattrup Smart, Simon Enni +20
We understand little about how capabilities acquired in one language carry over to another, or what governs this transfer: evaluations rely on incomparable, saturation-prone datasets and rarely examine its determinants jointly. Identifying what predicts transfer would let us avoid exhaustive evaluation across all language pairs and let developers target the factors that limit performance in low-resource languages. To evaluate cross-lingual capability transfer, we introduce Multilingual GSM-Symbolic, an extensible multilingual mathematical dataset covering 30,000 item-matched question-answer pairs and spanning 15 languages. It utilises symbolic templates to prevent overfitting and ensure generalisation by allowing generation of millions of high-quality variations from a single sample. Using Multilingual GSM-Symbolic, we quantify the largest determinants of capability as model size ($β= 1.77$), language resource level ($β= 0.77$), reasoning ($β= 0.67$) and typological distance ($β= -0.25$). This joint estimation allows these determinants to be expressed in terms of one another: a 32B model evaluated in Marathi performs like a 10B model in English. Our findings have important…
PDF arXiv Code
arXiv cs.CLCodeOct 2▲ 7
OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination
Huiqiang Rong, Haoran Luo, Hui Feng, Zhonghong Ou, Kaiwen Xue +2
Omni-modal large language models (OmniLLMs) unify text, images, audio, and video, yet hallucinate when generation relies on the wrong evidence. Existing inference-time methods can reduce hallucinations, but rarely reveal which evidence sustains a generated commitment. We introduce OmniConfess, a training-free method for mitigating omni-modal hallucinations. It fixes a candidate response and re-scores it at token resolution under controlled channel-wise evidence interventions, producing a structured token-by-channel confession that reveals the response's evidential dependence. OmniConfess uses this confession to preserve grounded content and correct commitments driven by irrelevant or contradictory evidence. To evaluate OmniConfess, we construct OmniHalluBench, a 3,540-example benchmark built from six datasets spanning text, image, audio, and video settings and both judgment and free-form generation. Experiments show that OmniConfess mitigates hallucinations across heterogeneous modality and task settings. Our code and benchmark are publicly available at https://github.com/RongHuiQiang/OmniConfess.
PDF arXiv Code
arXiv cs.CLCodeOct 1▲ 12
From Retrieval to Typed Decisions: Calibrated System One Models from Biomedical Sentence Encoders
Pritam Deka
Typed decision models answer schema-constrained questions about a text in one forward pass and return probabilities meant to be thresholded. We ask whether biomedical sentence encoders trained for retrieval are good starting points for such models. We present SBERT2S1, which converts Sentence-Transformers encoders into bi-encoder, cross-head (C) and prior-fused residual (PFR) decision models, together with BIODECIDE, a biomedical typed-decision suite, and MEDLINE-S1, 243k training decisions derived from NLM indexing. Across six parent-retriever pairs, retrieval training improves zero-shot matching of content-bearing options. After fine-tuning, its effect depends on the head: across five pairs and three training-set sizes, retrieval training significantly helps PFR, which keeps the retrieval prior, in 10 of 15 comparisons, but helps C in one and hurts it in five. A matched grid of two heads and five training objectives shows that C outperforms PFR under every objective, and that the released RLCD recipe of open System One models trails cross-entropy by 2.5-3.0 points. The deficit stems mainly from its reward normalisation, which inflates the noisy score-function term 3.6-15-fold;…
PDF arXiv Code
arXiv cs.LGCodeOct 1▲ 16
The Missing Primitive: Diagnosing and Repairing Mathematical Reasoning in Large Language Models
Shuo Xing, Zilin Dai, Chengyuan Qian, Fangzhou Lin, Wenjing Chen +5
While Large Language Models (LLMs) have demonstrated striking capabilities on frontier mathematical problems, it remains unclear whether they possess the structural mathematical understanding underlying their solutions. In this paper, we take a first step toward systematically studying mathematical understanding in LLMs, from diagnosing its distinct capabilities to leveraging these findings to improve post-training. First, we introduce the notion of Mathematical Primitive to probe structural mathematical understanding and propose \hlei{}, a novel benchmark that evaluates mathematical reasoning along four distinct dimensions: Discovery, Generation, Digestion, and Execution. Second, our systematic diagnosis shows that solution accuracy masks distinct capability profiles, primitives unlock substantial latent execution capacity, and Discovery is the dominant bottleneck in mathematical reasoning. Our post-training analysis further shows that discovery-limited failures are particularly amenable to repair. Finally, building on these findings, we introduce \abs{}, a primitive-privileged self-distillation framework that selectively transfers primitive-guided reasoning into the student…
PDF arXiv Code
arXiv cs.CLCodeOct 1▲ 2
Cross-Lingual Alignment for Decoder-Only Models using MoE Routers
Lucas Bandarkar, Clark Peng, Ahmed Haj Ahmed, Aditi Khandelwal, Nanyun Peng
Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is not possible in decoder-only LLMs because of varying multilingual tokenization. However, a growing amount of research suggests that even in LLMs, higher cross-lingual representational alignment leads to improved cross-lingual transfer. In this paper, we propose a novel approach to reimagine cross-lingual contrastive learning given the architectural constraints of modern LLMs. Rather than applying an auxiliary alignment loss on hidden states, we propose using the outputs of the mixture-of-experts (MoE) routers as the target for alignment. Router outputs lend themselves better to pooling over many tokens, enabling more reliable cross-lingual comparisons at the sequence-level. Controlled continual pre-training experiments on four open-source MoEs show that incorporating this routing loss also aligns the underlying hidden representations across languages. Most importantly, this loss improves multilingual performance on our diverse evaluation suite, demonstrating the potential of cross-lingual MoE router alignment.
PDF arXiv Code
arXiv cs.AICodeSep 30▲ 63
Science or Slop?: Benchmarking and Mitigating Scientific Slop in AI-Generated Papers
Yerim Oh, Young-Jun Lee, Jaewoo Ahn, Gunhee Kim, Dongyeop Kang
AI-generated content, often called AI slop, is increasingly common everywhere, particularly in academia. Slop in AI-generated scientific papers, however, has more complex patterns that cannot be easily detected by existing token-based AI detectors. Each part of such a paper looks plausible while the scientific reasoning that connects the parts breaks down, which can mislead how readers assess the work. We benchmark these failures as scientific slop through six measures across Structure, Argument, and Artifacts. We construct SciSlopBench with 390 AI-generated papers, mostly in computer science but spanning the life, social, and natural sciences, each paired with a human-written paper matched by research problem and contribution type. Our measures identify the AI paper in each pair with 85.9% accuracy, compared with 68.7% for Binoculars. Higher scientific slop accompanies lower ICLR ratings and distinguishes rejected from accepted papers above chance in every year from 2017 to 2025. Reducing these patterns, however, is not as simple as directly optimizing the measures. We therefore propose SciSlopHarness, a harness-level framework that guides a fixed LLM to revise slop only where…
PDF arXiv Code

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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%

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

Topics in this field

reasoning traces · reasoning models · latent reasoning381 papers75 this week
attention heads · large language models · language models231 papers55 this week
dense retrieval · retrieval augmented generation · retrieval256 papers53 this week
question answering · answering · large language models140 papers46 this week
large language models · attribution · language models llms203 papers43 this week
large language models · large language · language models184 papers40 this week

Active contributors · 90 days

Recent papers

All in Research →

arXiv cs.AICodeOct 2▲ 52
Multilingual GSM-Symbolic: What determines capability transfer across languages?
Kenneth Enevoldsen, Riley Herchert, Sofie Mosegaard, Dan Saattrup Smart, Simon Enni +20
We understand little about how capabilities acquired in one language carry over to another, or what governs this transfer: evaluations rely on incomparable, saturation-prone datasets and rarely examine its determinants jointly. Identifying what predicts transfer would let us avoid exhaustive evaluation across all language pairs and let developers target the factors that limit performance in low-resource languages. To evaluate cross-lingual capability transfer, we introduce Multilingual GSM-Symbolic, an extensible multilingual mathematical dataset covering 30,000 item-matched question-answer pairs and spanning 15 languages. It utilises symbolic templates to prevent overfitting and ensure generalisation by allowing generation of millions of high-quality variations from a single sample. Using Multilingual GSM-Symbolic, we quantify the largest determinants of capability as model size ($β= 1.77$), language resource level ($β= 0.77$), reasoning ($β= 0.67$) and typological distance ($β= -0.25$). This joint estimation allows these determinants to be expressed in terms of one another: a 32B model evaluated in Marathi performs like a 10B model in English. Our findings have important…
PDF arXiv Code
arXiv cs.CLCodeOct 2▲ 7
OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination
Huiqiang Rong, Haoran Luo, Hui Feng, Zhonghong Ou, Kaiwen Xue +2
Omni-modal large language models (OmniLLMs) unify text, images, audio, and video, yet hallucinate when generation relies on the wrong evidence. Existing inference-time methods can reduce hallucinations, but rarely reveal which evidence sustains a generated commitment. We introduce OmniConfess, a training-free method for mitigating omni-modal hallucinations. It fixes a candidate response and re-scores it at token resolution under controlled channel-wise evidence interventions, producing a structured token-by-channel confession that reveals the response's evidential dependence. OmniConfess uses this confession to preserve grounded content and correct commitments driven by irrelevant or contradictory evidence. To evaluate OmniConfess, we construct OmniHalluBench, a 3,540-example benchmark built from six datasets spanning text, image, audio, and video settings and both judgment and free-form generation. Experiments show that OmniConfess mitigates hallucinations across heterogeneous modality and task settings. Our code and benchmark are publicly available at https://github.com/RongHuiQiang/OmniConfess.
PDF arXiv Code
arXiv cs.CLCodeOct 1▲ 12
From Retrieval to Typed Decisions: Calibrated System One Models from Biomedical Sentence Encoders
Pritam Deka
Typed decision models answer schema-constrained questions about a text in one forward pass and return probabilities meant to be thresholded. We ask whether biomedical sentence encoders trained for retrieval are good starting points for such models. We present SBERT2S1, which converts Sentence-Transformers encoders into bi-encoder, cross-head (C) and prior-fused residual (PFR) decision models, together with BIODECIDE, a biomedical typed-decision suite, and MEDLINE-S1, 243k training decisions derived from NLM indexing. Across six parent-retriever pairs, retrieval training improves zero-shot matching of content-bearing options. After fine-tuning, its effect depends on the head: across five pairs and three training-set sizes, retrieval training significantly helps PFR, which keeps the retrieval prior, in 10 of 15 comparisons, but helps C in one and hurts it in five. A matched grid of two heads and five training objectives shows that C outperforms PFR under every objective, and that the released RLCD recipe of open System One models trails cross-entropy by 2.5-3.0 points. The deficit stems mainly from its reward normalisation, which inflates the noisy score-function term 3.6-15-fold;…
PDF arXiv Code
arXiv cs.LGCodeOct 1▲ 16
The Missing Primitive: Diagnosing and Repairing Mathematical Reasoning in Large Language Models
Shuo Xing, Zilin Dai, Chengyuan Qian, Fangzhou Lin, Wenjing Chen +5
While Large Language Models (LLMs) have demonstrated striking capabilities on frontier mathematical problems, it remains unclear whether they possess the structural mathematical understanding underlying their solutions. In this paper, we take a first step toward systematically studying mathematical understanding in LLMs, from diagnosing its distinct capabilities to leveraging these findings to improve post-training. First, we introduce the notion of Mathematical Primitive to probe structural mathematical understanding and propose \hlei{}, a novel benchmark that evaluates mathematical reasoning along four distinct dimensions: Discovery, Generation, Digestion, and Execution. Second, our systematic diagnosis shows that solution accuracy masks distinct capability profiles, primitives unlock substantial latent execution capacity, and Discovery is the dominant bottleneck in mathematical reasoning. Our post-training analysis further shows that discovery-limited failures are particularly amenable to repair. Finally, building on these findings, we introduce \abs{}, a primitive-privileged self-distillation framework that selectively transfers primitive-guided reasoning into the student…
PDF arXiv Code
arXiv cs.CLCodeOct 1▲ 2
Cross-Lingual Alignment for Decoder-Only Models using MoE Routers
Lucas Bandarkar, Clark Peng, Ahmed Haj Ahmed, Aditi Khandelwal, Nanyun Peng
Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is not possible in decoder-only LLMs because of varying multilingual tokenization. However, a growing amount of research suggests that even in LLMs, higher cross-lingual representational alignment leads to improved cross-lingual transfer. In this paper, we propose a novel approach to reimagine cross-lingual contrastive learning given the architectural constraints of modern LLMs. Rather than applying an auxiliary alignment loss on hidden states, we propose using the outputs of the mixture-of-experts (MoE) routers as the target for alignment. Router outputs lend themselves better to pooling over many tokens, enabling more reliable cross-lingual comparisons at the sequence-level. Controlled continual pre-training experiments on four open-source MoEs show that incorporating this routing loss also aligns the underlying hidden representations across languages. Most importantly, this loss improves multilingual performance on our diverse evaluation suite, demonstrating the potential of cross-lingual MoE router alignment.
PDF arXiv Code
arXiv cs.AICodeSep 30▲ 63
Science or Slop?: Benchmarking and Mitigating Scientific Slop in AI-Generated Papers
Yerim Oh, Young-Jun Lee, Jaewoo Ahn, Gunhee Kim, Dongyeop Kang
AI-generated content, often called AI slop, is increasingly common everywhere, particularly in academia. Slop in AI-generated scientific papers, however, has more complex patterns that cannot be easily detected by existing token-based AI detectors. Each part of such a paper looks plausible while the scientific reasoning that connects the parts breaks down, which can mislead how readers assess the work. We benchmark these failures as scientific slop through six measures across Structure, Argument, and Artifacts. We construct SciSlopBench with 390 AI-generated papers, mostly in computer science but spanning the life, social, and natural sciences, each paired with a human-written paper matched by research problem and contribution type. Our measures identify the AI paper in each pair with 85.9% accuracy, compared with 68.7% for Binoculars. Higher scientific slop accompanies lower ICLR ratings and distinguishes rejected from accepted papers above chance in every year from 2017 to 2025. Reducing these patterns, however, is not as simple as directly optimizing the measures. We therefore propose SciSlopHarness, a harness-level framework that guides a fixed LLM to revise slop only where…
PDF arXiv Code

Activity · 12 weeks

Coming with more sources: repos

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.

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