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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.

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Recently funded

Topics in this field

reasoning traces · reasoning models · latent reasoning381 papers64 this week
dense retrieval · retrieval augmented generation · retrieval256 papers50 this week
attention heads · large language models · language models231 papers49 this week
large language models · attribution · language models llms203 papers41 this week
question answering · answering · large language models140 papers40 this week
large language models · large language · language models184 papers36 this week

Topics in this field

Active contributors · 90 days

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arXiv cs.LGOct 16
LangNCO: Compiling Natural-Language Constraints into Inference-Time Interventions for Neural Combinatorial Optimization
Hironori Ohigashi
Neural combinatorial optimization (NCO) rapidly produces high-quality solutions for problem structures encountered during training, but operational requirements may change after deployment and be communicated in natural language. We propose LangNCO, a framework that compiles instance-specific requirements into structured inference-time interventions for a frozen NCO solver, without changing the pretrained policy's architecture, learned input representation, or parameters. A pretrained LLM serves as a replaceable semantic front end, translating each requirement into a schema-validated domain-specific language (DSL) rule set. A deterministic Rule Executor applies the rules as nonexpansive hard action masks. Because local masking prevents immediate violations but does not ensure that a complete feasible continuation remains reachable, Critical-Offset Decoding extracts rule-referenced critical nodes and reallocates part of a fixed rollout budget to multi-strength biased rollouts. Using controlled task-specific language to isolate solver adaptation from open-ended language robustness, we evaluate a CVRP policy on road closures, time windows, vehicle assignments, visit-order…
PDF arXiv
arXiv cs.CLOct 16
AMEND: Constraint-Guided Multi-Agent Text Detoxification with Isolated Critics
Tianyu Wang
Text detoxification requires balancing toxicity removal, semantic preservation, and fluency. Existing methods typically optimize an unconstrained scalar objective---enabling safety scores to mask semantic loss---or rely on open-ended iterative debate prone to semantic drift and high computational cost. We propose AMEND, an inference-time framework that decouples detoxification into role-conditioned proposal generation, isolated multidimensional criticism, and constraint-first selection. AMEND evaluates diverse candidates with role-blind critics and enforces strict semantic and fluency feasibility before ranking by utility. To maximize efficiency, AMEND Lite restricts subsequent search to at most one targeted repair round only when residual toxic spans remain. On ParaDetox, one-round selection over diverse roles improves the joint evaluation metric by $+0.0508$ over the best single role. On a frozen confirmation test set, AMEND Lite achieves a joint score of $0.6597 \pm 0.0062$, outperforming unconstrained multi-round loops by $+0.0489$ ($p < 0.05$) while reducing inference requests by 28.4%.
PDF arXiv
arXiv cs.CLOct 16
What LLMs Do in the Shadows: Reading Context Reliance from the Unembedding Dark Space
Alexandra Kuleshova, Andrey Volodichev, Daria Kotova, Alexey Zaytsev
Detecting faithfulness hallucinations in large language models (LLMs) is crucial for their safe deployment in high-stakes domains. Recent approaches address this by contrasting contextual against parametric knowledge utilization, yet measuring context reliance typically demands noticeable overhead, such as materializing attention maps, running additional forward passes, or training auxiliary probes. In this work, we show that context reliance can be estimated by projecting hidden states onto the dark space - directions that compose weakly with the unembedding matrix. Beyond its recognized function as an initial-token attention sink, we show that this subspace actively mediates context processing, and that its share of hidden-state norm - termed the Dark Matter Ratio (DMR) - provides a direct score of context utilization. Pairing DMR with a parametric knowledge score yields DarkLens, a lightweight faithfulness detector operating strictly within a single forward pass. Evaluated across three QA and summarization benchmarks on six open-weight LLMs (4B-13B parameters), DarkLens matches or outperforms a wide range of baselines without requiring extra forward passes or probe training.
PDF arXiv
arXiv cs.CLOct 16
Chinese Decision: A Family of Language Models for Single-Pass Probabilistic Decision-Making
Qiang Yan
Many language-based applications require probabilities over a finite set of decisions rather than open-ended responses. We present Chinese Decision, a family of Chinese-language models for single-pass probabilistic decision-making. Given context, an instruction, and candidate descriptions, the model extracts final-position candidate-letter logits in one forward pass and normalizes them over valid options, without autoregressive answer generation. A shared interface supports Boolean judgments, categorical choices, and ordinal scores. We report a completed 0.8B model trained with distribution cross-entropy on 13.048 million decisions and preliminary 2B checkpoints trained with an additional RLCD objective. On the full CJ-Bench of 307,900 decisions, the uncalibrated 0.8B model achieves 72.24% general-domain accuracy and 76.98% average accuracy across medicine, law, and finance using a single checkpoint, with 1.07% overall expected calibration error. The 2B model reaches 72.56% general-domain accuracy after 40.13% of one epoch but has not surpassed the completed 0.8B model on the professional-domain average. We further evaluate a validation-temperature-scaled deployment model and its…
PDF arXiv
arXiv cs.AIOct 16
LongForge: Measuring and Improving Long-Context Instruction Data via Evidence-Assertion Reasoning Graphs
Ziyang Chen, Junlong Jia, Xing Wu, Tinghao Yu, Songlin Hu
Existing quality control for long-context instruction-tuning data is a one-shot judgment inside the generation pipeline that leaves only a scalar, revealing neither which document parts an answer uses nor how many reasoning steps it writes down; the data can thus be neither audited nor controlled. We formalize data quality as a property of the alignment structure among document, question, and answer, and build LongForge, which pairs an analyzer that measures long-context data with a synthesizer that generates it. The analyzer reverse-reconstructs each sample into an evidence-assertion reasoning graph and programmatically computes four dimensions on it: breadth, depth, correctness, and completeness. Every reading traces back to specific evidence spans and reasoning steps, making data auditable. Measuring seven mainstream datasets reveals two previously unquantified defects: breadth barely grows with document length, and depth is generally low, the two being nearly orthogonal. The synthesizer then embeds the analyzer into generation, gating on correctness and completeness and deferring breadth and depth to a training-time mixture over measured values, which makes the training…
PDF arXiv
arXiv cs.CLOct 16
Explaining Chess and Go in Natural Language
Ori Press, Alexander Golubev
Modern chess engines play far beyond human ability, often choosing moves whose underlying ideas are difficult for humans to understand. We propose an automatic metric for evaluating chess explanations, grounded in chess-engine evaluations. Evaluating state-of-the-art language models using this metric, we find that they perform surprisingly poorly, often relying on surface-level board observations rather than robust, transferable chess concepts. We then introduce BinProp, a training-free method that enables these same models to discover reusable chess concepts and use them to explain moves in natural language. BinProp improves explainability for every model tested, nearly tripling the average score compared with direct prompting. Beyond chess, the same method discovers useful concepts in Go, draughts, and shogi without additional tuning.
PDF arXiv

Papers

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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%

Topics in this field

reasoning traces · reasoning models · latent reasoning381 papers64 this week
dense retrieval · retrieval augmented generation · retrieval256 papers50 this week
attention heads · large language models · language models231 papers49 this week
large language models · attribution · language models llms203 papers41 this week
question answering · answering · large language models140 papers40 this week
large language models · large language · language models184 papers36 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

Coming with more sources: repos

Recent papers

All in Research →

arXiv cs.LGOct 16
LangNCO: Compiling Natural-Language Constraints into Inference-Time Interventions for Neural Combinatorial Optimization
Hironori Ohigashi
Neural combinatorial optimization (NCO) rapidly produces high-quality solutions for problem structures encountered during training, but operational requirements may change after deployment and be communicated in natural language. We propose LangNCO, a framework that compiles instance-specific requirements into structured inference-time interventions for a frozen NCO solver, without changing the pretrained policy's architecture, learned input representation, or parameters. A pretrained LLM serves as a replaceable semantic front end, translating each requirement into a schema-validated domain-specific language (DSL) rule set. A deterministic Rule Executor applies the rules as nonexpansive hard action masks. Because local masking prevents immediate violations but does not ensure that a complete feasible continuation remains reachable, Critical-Offset Decoding extracts rule-referenced critical nodes and reallocates part of a fixed rollout budget to multi-strength biased rollouts. Using controlled task-specific language to isolate solver adaptation from open-ended language robustness, we evaluate a CVRP policy on road closures, time windows, vehicle assignments, visit-order…
PDF arXiv
arXiv cs.CLOct 16
AMEND: Constraint-Guided Multi-Agent Text Detoxification with Isolated Critics
Tianyu Wang
Text detoxification requires balancing toxicity removal, semantic preservation, and fluency. Existing methods typically optimize an unconstrained scalar objective---enabling safety scores to mask semantic loss---or rely on open-ended iterative debate prone to semantic drift and high computational cost. We propose AMEND, an inference-time framework that decouples detoxification into role-conditioned proposal generation, isolated multidimensional criticism, and constraint-first selection. AMEND evaluates diverse candidates with role-blind critics and enforces strict semantic and fluency feasibility before ranking by utility. To maximize efficiency, AMEND Lite restricts subsequent search to at most one targeted repair round only when residual toxic spans remain. On ParaDetox, one-round selection over diverse roles improves the joint evaluation metric by $+0.0508$ over the best single role. On a frozen confirmation test set, AMEND Lite achieves a joint score of $0.6597 \pm 0.0062$, outperforming unconstrained multi-round loops by $+0.0489$ ($p < 0.05$) while reducing inference requests by 28.4%.
PDF arXiv
arXiv cs.CLOct 16
What LLMs Do in the Shadows: Reading Context Reliance from the Unembedding Dark Space
Alexandra Kuleshova, Andrey Volodichev, Daria Kotova, Alexey Zaytsev
Detecting faithfulness hallucinations in large language models (LLMs) is crucial for their safe deployment in high-stakes domains. Recent approaches address this by contrasting contextual against parametric knowledge utilization, yet measuring context reliance typically demands noticeable overhead, such as materializing attention maps, running additional forward passes, or training auxiliary probes. In this work, we show that context reliance can be estimated by projecting hidden states onto the dark space - directions that compose weakly with the unembedding matrix. Beyond its recognized function as an initial-token attention sink, we show that this subspace actively mediates context processing, and that its share of hidden-state norm - termed the Dark Matter Ratio (DMR) - provides a direct score of context utilization. Pairing DMR with a parametric knowledge score yields DarkLens, a lightweight faithfulness detector operating strictly within a single forward pass. Evaluated across three QA and summarization benchmarks on six open-weight LLMs (4B-13B parameters), DarkLens matches or outperforms a wide range of baselines without requiring extra forward passes or probe training.
PDF arXiv
arXiv cs.CLOct 16
Chinese Decision: A Family of Language Models for Single-Pass Probabilistic Decision-Making
Qiang Yan
Many language-based applications require probabilities over a finite set of decisions rather than open-ended responses. We present Chinese Decision, a family of Chinese-language models for single-pass probabilistic decision-making. Given context, an instruction, and candidate descriptions, the model extracts final-position candidate-letter logits in one forward pass and normalizes them over valid options, without autoregressive answer generation. A shared interface supports Boolean judgments, categorical choices, and ordinal scores. We report a completed 0.8B model trained with distribution cross-entropy on 13.048 million decisions and preliminary 2B checkpoints trained with an additional RLCD objective. On the full CJ-Bench of 307,900 decisions, the uncalibrated 0.8B model achieves 72.24% general-domain accuracy and 76.98% average accuracy across medicine, law, and finance using a single checkpoint, with 1.07% overall expected calibration error. The 2B model reaches 72.56% general-domain accuracy after 40.13% of one epoch but has not surpassed the completed 0.8B model on the professional-domain average. We further evaluate a validation-temperature-scaled deployment model and its…
PDF arXiv
arXiv cs.AIOct 16
LongForge: Measuring and Improving Long-Context Instruction Data via Evidence-Assertion Reasoning Graphs
Ziyang Chen, Junlong Jia, Xing Wu, Tinghao Yu, Songlin Hu
Existing quality control for long-context instruction-tuning data is a one-shot judgment inside the generation pipeline that leaves only a scalar, revealing neither which document parts an answer uses nor how many reasoning steps it writes down; the data can thus be neither audited nor controlled. We formalize data quality as a property of the alignment structure among document, question, and answer, and build LongForge, which pairs an analyzer that measures long-context data with a synthesizer that generates it. The analyzer reverse-reconstructs each sample into an evidence-assertion reasoning graph and programmatically computes four dimensions on it: breadth, depth, correctness, and completeness. Every reading traces back to specific evidence spans and reasoning steps, making data auditable. Measuring seven mainstream datasets reveals two previously unquantified defects: breadth barely grows with document length, and depth is generally low, the two being nearly orthogonal. The synthesizer then embeds the analyzer into generation, gating on correctness and completeness and deferring breadth and depth to a training-time mixture over measured values, which makes the training…
PDF arXiv
arXiv cs.CLOct 16
Explaining Chess and Go in Natural Language
Ori Press, Alexander Golubev
Modern chess engines play far beyond human ability, often choosing moves whose underlying ideas are difficult for humans to understand. We propose an automatic metric for evaluating chess explanations, grounded in chess-engine evaluations. Evaluating state-of-the-art language models using this metric, we find that they perform surprisingly poorly, often relying on surface-level board observations rather than robust, transferable chess concepts. We then introduce BinProp, a training-free method that enables these same models to discover reusable chess concepts and use them to explain moves in natural language. BinProp improves explainability for every model tested, nearly tripling the average score compared with direct prompting. Beyond chess, the same method discovers useful concepts in Go, draughts, and shogi without additional tuning.
PDF arXiv

Fields · AI · updated 16m ago

Frontier LLMs

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2.2k 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.