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Open problems in Frontier LLMs

Reliable and efficient decision-making for LLM systems4 papers raise it · 4 address it
Fast, accurate, and calibrated decisions are needed for routing, guardrails, and verification in LLM agents and pipelines.
- Jeva: System One Models from First Principlesraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
- DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systemsraises · addresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsraises · addresses
Memory consolidation for context-dependent gains1 paper raises it · 1 addresses it
Models often lose performance improvements after context is removed, limiting long-term retention of learned skills or edits.
- Cortex: Consolidating Context into Parametric Memoryraises · addresses
Sustained self-evolution in reasoning models1 paper raises it · 1 addresses it
Self-training on generated questions can collapse due to invalid or repetitive questions, degrading performance over time.
- Questioning the Questions: Sustaining Self-Evolution in Reasoning Modelsraises · addresses
No-thinking compliance in LLMs1 paper raises it · 1 addresses it
LLMs often ignore or poorly follow instructions to avoid reasoning, leading to unreliable or non-compliant outputs.
- Thinking Inertia: LLMs Keep Thinking When Told Not Toraises · addresses
End-to-end deep search in real-world web environments1 paper raises it · 1 addresses it
Agents struggle to autonomously explore, integrate, and verify evidence from diverse, interactive, and heterogeneous web sources.
- Deep Search in Real-World Web Environmentsraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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PennantSeed · Oct 20$5M

Recently funded

Topics in this field

reasoning traces · reasoning models · latent reasoning405 papers54 this week
dense retrieval · retrieval augmented generation · retrieval278 papers50 this week
question answering · answering · large language models154 papers40 this week
attention heads · large language models · language models241 papers36 this week
large language models · attribution · language models llms219 papers32 this week
large language models · large language · language models196 papers27 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 8
Conditional Transfer from Controlled Pretraining Mixtures to Code
Ohad Rubin
Synthetic tasks are increasingly used both as probes of language-model capability and as pretraining data. Both uses are often justified by loss reduction: falling loss is treated as informative, and faster loss reduction with more sampling as evidence that a task is worth sampling. We separate three signals. A task is diagnostic when its loss tracks global pretraining progress; it is teachable when its loss responds to its own token budget; and a data source transfers when including it improves a downstream target. We study controlled pretraining in which 70% of the corpus is fixed general Python and the remaining 30% is a simplex over three source families: OpenCodeInstruct, a curated suite of 12 code-adjacent synthetic tasks, and 15 literature-derived probe tasks. Across a task-budget sweep we detect teachability for 14 of 27 tasks, with a sharp asymmetry between the two synthetic families (10/12 curated versus 4/15 literature-derived). Teachability and downstream transfer give different rankings. On the mixture-simplex edge between the curated suite and OpenCodeInstruct, HumanEval pass@20 after a fixed fine-tuning stage rises from 15.9 at pure curated data to its highest…
PDF arXiv
arXiv cs.HCOct 8
Design Creativity Bench: Measuring creativity in LLM-Generated UI
Aman Rusia, Abhijit Bhole, Prashank Gupta, Dipanjan Dey
As leading LLMs improve on capability evaluations, their limitations in producing creative outputs on design tasks remain insufficiently characterised. Our work introduces Design Creativity Bench, a benchmark that evaluates diversity and appropriateness in UI designs. It measures distinctiveness among models on the same prompt (originality), how much a model's designs change between two prompts for the same UI goal in different product domains (creative range), and the share of a brief's acceptance criteria each design meets (appropriateness). Originality is 0.592 for same-prompt design pairs from different models (95% CI [0.582, 0.602]), far below the 0.764 for same-prompt human-model pairs (95% CI [0.751, 0.778]). Creative range is 0.581 across models (95% CI [0.567, 0.597]), against 0.902 for human designs (95% CI [0.884, 0.919]). Appropriateness is above 90% for every model in our test set, and the best model reaches 99.2%, slightly above the 98.0% for human designs. Our work shows that the default output of LLMs, though generally appropriate, is substantially more repetitive than the human baseline. This calls for strong measures to address the issue. A live leaderboard with…
PDF arXiv
arXiv cs.CLOct 8
Does Modern Standard Arabic (MSA) Dominate Arabic Dialects in LLMs? A Representation-Level Analysis
Abdu Sallouh, Nicholas Popovič, Michael Färber
Large language models (LLMs) often default to Modern Standard Arabic (MSA) when generating Arabic, even when prompted with dialectal Arabic. A natural explanation is that their internal representations are dominated by MSA. We test this hypothesis by adapting the language-dominance framework of Shani and Basirat (2025) (https://doi.org/10.18653/v1/2025.blackboxnlp-1.7) to 26 Arabic varieties. Across layers and model families, we find no evidence that MSA acts as a dominant internal representation for Arabic dialects. Instead, dialect representations form a dense and highly overlapping space: normalized mutual information drops sharply compared to patterns reported for more distinct languages. Moreover, the strongest separability effects are not confined to intermediate layers, but can shift toward later layers depending on the architecture. These findings challenge a common interpretation of MSA-biased generation: output preference does not necessarily reveal internal representational dominance. Analyses of multilingual and dialectal LLMs should therefore distinguish generation bias from the geometry of internal representations.
PDF arXiv
arXiv cs.CLOct 8
Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation
Leikun Liang, Guoshuai Wang, Xingsheng He, Yushan Han, Yunyi Xuan +2
Despite the adoption of large language models (LLMs) in recommendation systems, prevailing approaches mostly model single-type behaviors (e.g., views or purchases). Even when incorporating multiple behaviors, existing methods flatten heterogeneous actions into homogeneous token sequences, ignoring their distinct decision-making roles. This flattening fails to capture semantic hierarchies and contextual nuances in complex decision-making, such as trade-offs between price and quality. Consequently, performance degrades in critical ``difficult-choice'' scenarios involving highly similar items. To bridge this gap, we propose MARI (Memory-Augmented Recommendation with Interpretability), which grounds predictions in explicit, structured decision evidence. MARI maintains a Decision Memory Bank (DMB) that archives users' past rationales as Structured Decision Memories (SDMs): concise records of goals, constraints, and trade-offs. These SDMs are generated offline via Post-Hoc Decision Distillation from heterogeneous behaviors and user-generated content. By retrieving relevant SDMs to augment LLM reasoning, MARI achieves interpretability and scalability without the prohibitive cost of…
PDF arXiv
arXiv cs.AIOct 8
From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers
Gerard Grau García, Arnau Padrés Masdemont, Niccolò Grillo, Jordi Ros-Giralt, Arash Behboodi +1
Chain-of-thought (CoT) reasoning often improves language-model performance by giving models additional computation before answering. However, explicit CoT expresses this computation as a sequence of autoregressively generated tokens. Latent reasoning replaces these tokens with compact continuous states, but most autoregressive latent-reasoning methods retain a left-to-right dependency among latent vectors. We introduce LLoCoT: a looped latent-reasoning framework that replaces left-to-right latent generation with iterative refinement of a compact latent workspace. A shared transformer is reapplied for a small number of refinement iterations, jointly updating the latent slots based on the prompt and the evolving workspace state. Using the refined state, a probabilistic head predicts a distribution from which latent tokens are sampled in parallel and used to condition an autoregressive decoder for answer generation. Training uses continuous representations derived from explicit CoT together with a final-answer prediction loss and likelihood-based supervision of the latent states. Across HumanEval and MBPP, LLoCoT achieves the highest mean among the evaluated methods, performing on…
PDF arXiv
arXiv cs.CROct 8
GROB: A Multi-Agent Architecture for Public-Trace Investigation of Candidate Agentic Activity
Chiara Bonfanti, Cataldo Basile
We present GROB, a multi-agent architecture for investigating candidate autonomous-agent activity through public Internet traces when privileged telemetry is unavailable. The system performs controlled, read-only collection of public traces and preserves selected observations for later resolution. In a frozen September 2026 corpus, several collected traces became more informative as additional public evidence emerged. The strongest result concerns Census-labelled identifiers captured on 9 September. Public revision records later resolved these identifiers to specific Census requests from 16 - 17 June. Other results show weaker links between traces collected by GROB and evidence reconstructed or reported later. These links vary in strength, and only some can be tied to specific public records. The results show that sparse public traces can remain useful even before their significance is fully understood. Such evidence can support later reconstruction, but public traces alone do not establish organizational attribution. Execution identity presents a separate problem, as continuity of agent identity remains an active research question for autonomous language-model agents.
PDF arXiv

Papers

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QwenPaw+467% downloads · 29.9k/wk+466.8%
jcode+233% downloads · 25.5k/wk+233.1%
billion-context+259% downloads · 1.1M/wk+258.8%
NVIDIA Dynamo+79% downloads · 17k/wk+79.1%
oh-my-claudecode+47% downloads · 7.7k/wk+47.4%

Topics in this field

reasoning traces · reasoning models · latent reasoning405 papers54 this week
dense retrieval · retrieval augmented generation · retrieval278 papers50 this week
question answering · answering · large language models154 papers40 this week
attention heads · large language models · language models241 papers36 this week
large language models · attribution · language models llms219 papers32 this week
large language models · large language · language models196 papers27 this week

Recently funded

Market

OpenAIStage not given · Oct 22$30B
DeepSeekStage not given · Oct 22$15B
Flapping AirplanesSeries A · Oct 21$1B
Electric PlantSeed · Oct 20$11.2M
PennantSeed · Oct 20$5M

Active contributors · 90 days

Activity · 12 weeks

Fields · AI · updated 22m ago

Frontier LLMs

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

385papers this week−25.2% vs last week

All papers · 2.4k With code · 63

Newest Most relevant

2.1k papers this month

Open problems in Frontier LLMs

Reliable and efficient decision-making for LLM systems4 papers raise it · 4 address it
Fast, accurate, and calibrated decisions are needed for routing, guardrails, and verification in LLM agents and pipelines.
- Jeva: System One Models from First Principlesraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
- DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systemsraises · addresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsraises · addresses
Memory consolidation for context-dependent gains1 paper raises it · 1 addresses it
Models often lose performance improvements after context is removed, limiting long-term retention of learned skills or edits.
- Cortex: Consolidating Context into Parametric Memoryraises · addresses
Sustained self-evolution in reasoning models1 paper raises it · 1 addresses it
Self-training on generated questions can collapse due to invalid or repetitive questions, degrading performance over time.
- Questioning the Questions: Sustaining Self-Evolution in Reasoning Modelsraises · addresses
No-thinking compliance in LLMs1 paper raises it · 1 addresses it
LLMs often ignore or poorly follow instructions to avoid reasoning, leading to unreliable or non-compliant outputs.
- Thinking Inertia: LLMs Keep Thinking When Told Not Toraises · addresses
End-to-end deep search in real-world web environments1 paper raises it · 1 addresses it
Agents struggle to autonomously explore, integrate, and verify evidence from diverse, interactive, and heterogeneous web sources.
- Deep Search in Real-World Web Environmentsraises · addresses

Named by an AI from these papers' abstracts.

Recent papers

All papers in Research

arXiv cs.LGOct 8
Conditional Transfer from Controlled Pretraining Mixtures to Code
Ohad Rubin
Synthetic tasks are increasingly used both as probes of language-model capability and as pretraining data. Both uses are often justified by loss reduction: falling loss is treated as informative, and faster loss reduction with more sampling as evidence that a task is worth sampling. We separate three signals. A task is diagnostic when its loss tracks global pretraining progress; it is teachable when its loss responds to its own token budget; and a data source transfers when including it improves a downstream target. We study controlled pretraining in which 70% of the corpus is fixed general Python and the remaining 30% is a simplex over three source families: OpenCodeInstruct, a curated suite of 12 code-adjacent synthetic tasks, and 15 literature-derived probe tasks. Across a task-budget sweep we detect teachability for 14 of 27 tasks, with a sharp asymmetry between the two synthetic families (10/12 curated versus 4/15 literature-derived). Teachability and downstream transfer give different rankings. On the mixture-simplex edge between the curated suite and OpenCodeInstruct, HumanEval pass@20 after a fixed fine-tuning stage rises from 15.9 at pure curated data to its highest…
PDF arXiv
arXiv cs.HCOct 8
Design Creativity Bench: Measuring creativity in LLM-Generated UI
Aman Rusia, Abhijit Bhole, Prashank Gupta, Dipanjan Dey
As leading LLMs improve on capability evaluations, their limitations in producing creative outputs on design tasks remain insufficiently characterised. Our work introduces Design Creativity Bench, a benchmark that evaluates diversity and appropriateness in UI designs. It measures distinctiveness among models on the same prompt (originality), how much a model's designs change between two prompts for the same UI goal in different product domains (creative range), and the share of a brief's acceptance criteria each design meets (appropriateness). Originality is 0.592 for same-prompt design pairs from different models (95% CI [0.582, 0.602]), far below the 0.764 for same-prompt human-model pairs (95% CI [0.751, 0.778]). Creative range is 0.581 across models (95% CI [0.567, 0.597]), against 0.902 for human designs (95% CI [0.884, 0.919]). Appropriateness is above 90% for every model in our test set, and the best model reaches 99.2%, slightly above the 98.0% for human designs. Our work shows that the default output of LLMs, though generally appropriate, is substantially more repetitive than the human baseline. This calls for strong measures to address the issue. A live leaderboard with…
PDF arXiv
arXiv cs.CLOct 8
Does Modern Standard Arabic (MSA) Dominate Arabic Dialects in LLMs? A Representation-Level Analysis
Abdu Sallouh, Nicholas Popovič, Michael Färber
Large language models (LLMs) often default to Modern Standard Arabic (MSA) when generating Arabic, even when prompted with dialectal Arabic. A natural explanation is that their internal representations are dominated by MSA. We test this hypothesis by adapting the language-dominance framework of Shani and Basirat (2025) (https://doi.org/10.18653/v1/2025.blackboxnlp-1.7) to 26 Arabic varieties. Across layers and model families, we find no evidence that MSA acts as a dominant internal representation for Arabic dialects. Instead, dialect representations form a dense and highly overlapping space: normalized mutual information drops sharply compared to patterns reported for more distinct languages. Moreover, the strongest separability effects are not confined to intermediate layers, but can shift toward later layers depending on the architecture. These findings challenge a common interpretation of MSA-biased generation: output preference does not necessarily reveal internal representational dominance. Analyses of multilingual and dialectal LLMs should therefore distinguish generation bias from the geometry of internal representations.
PDF arXiv
arXiv cs.CLOct 8
Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation
Leikun Liang, Guoshuai Wang, Xingsheng He, Yushan Han, Yunyi Xuan +2
Despite the adoption of large language models (LLMs) in recommendation systems, prevailing approaches mostly model single-type behaviors (e.g., views or purchases). Even when incorporating multiple behaviors, existing methods flatten heterogeneous actions into homogeneous token sequences, ignoring their distinct decision-making roles. This flattening fails to capture semantic hierarchies and contextual nuances in complex decision-making, such as trade-offs between price and quality. Consequently, performance degrades in critical ``difficult-choice'' scenarios involving highly similar items. To bridge this gap, we propose MARI (Memory-Augmented Recommendation with Interpretability), which grounds predictions in explicit, structured decision evidence. MARI maintains a Decision Memory Bank (DMB) that archives users' past rationales as Structured Decision Memories (SDMs): concise records of goals, constraints, and trade-offs. These SDMs are generated offline via Post-Hoc Decision Distillation from heterogeneous behaviors and user-generated content. By retrieving relevant SDMs to augment LLM reasoning, MARI achieves interpretability and scalability without the prohibitive cost of…
PDF arXiv
arXiv cs.AIOct 8
From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers
Gerard Grau García, Arnau Padrés Masdemont, Niccolò Grillo, Jordi Ros-Giralt, Arash Behboodi +1
Chain-of-thought (CoT) reasoning often improves language-model performance by giving models additional computation before answering. However, explicit CoT expresses this computation as a sequence of autoregressively generated tokens. Latent reasoning replaces these tokens with compact continuous states, but most autoregressive latent-reasoning methods retain a left-to-right dependency among latent vectors. We introduce LLoCoT: a looped latent-reasoning framework that replaces left-to-right latent generation with iterative refinement of a compact latent workspace. A shared transformer is reapplied for a small number of refinement iterations, jointly updating the latent slots based on the prompt and the evolving workspace state. Using the refined state, a probabilistic head predicts a distribution from which latent tokens are sampled in parallel and used to condition an autoregressive decoder for answer generation. Training uses continuous representations derived from explicit CoT together with a final-answer prediction loss and likelihood-based supervision of the latent states. Across HumanEval and MBPP, LLoCoT achieves the highest mean among the evaluated methods, performing on…
PDF arXiv
arXiv cs.CROct 8
GROB: A Multi-Agent Architecture for Public-Trace Investigation of Candidate Agentic Activity
Chiara Bonfanti, Cataldo Basile
We present GROB, a multi-agent architecture for investigating candidate autonomous-agent activity through public Internet traces when privileged telemetry is unavailable. The system performs controlled, read-only collection of public traces and preserves selected observations for later resolution. In a frozen September 2026 corpus, several collected traces became more informative as additional public evidence emerged. The strongest result concerns Census-labelled identifiers captured on 9 September. Public revision records later resolved these identifiers to specific Census requests from 16 - 17 June. Other results show weaker links between traces collected by GROB and evidence reconstructed or reported later. These links vary in strength, and only some can be tied to specific public records. The results show that sparse public traces can remain useful even before their significance is fully understood. Such evidence can support later reconstruction, but public traces alone do not establish organizational attribution. Execution identity presents a separate problem, as continuity of agent identity remains an active research question for autonomous language-model agents.
PDF arXiv