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Open problems in Frontier LLMs

Evaluating and benchmarking decision models for reliability and calibration3 papers raise it · 4 address it
Decision models are increasingly used in LLM systems but lack standardized ways to measure correctness, confidence, and robustness across controlled variations.
- DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systemsraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
- Jeva: System One Models from First Principlesraises · addresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsaddresses
Efficient and accurate fact-checking of AI and human reviews1 paper raises it · 1 addresses it
Peer review systems struggle with factual errors in comments, and current methods are either too slow or inaccurate for large-scale use.
- PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviewsraises · addresses
Long-horizon agent context management and memory retention2 papers raise it · 3 address it
Agents lose critical information when context windows are exceeded, and current compaction methods fail to preserve decision-relevant details.
- REMORY: Learning Residual Memory for Context Compactionraises · addresses
- Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agentsraises · addresses
- Cortex: Consolidating Context into Parametric Memoryaddresses
Real-world web-based deep search and evidence integration1 paper raises it · 1 addresses it
Agents cannot reliably perform multi-step research in live, open web environments with diverse, interactive, and heterogeneous information sources.
- Deep Search in Real-World Web Environmentsraises · addresses
User behavior modeling for long-term personalization1 paper raises it · 1 addresses it
LLMs lack the ability to infer and adapt to evolving user preferences and intent from real-world behavior data over time.
- Vela: User Behavior as Supervision Sparks LLM Intelligenceraises · addresses

Named by an AI from these papers' abstracts.

Open problems

Recently funded

Market

Chaoqiumian TechnologySeed · Oct 26undisclosed
HypersphereSeed · Oct 26undisclosed
Star TherapeuticsSeries B+ · Oct 23$194.9M
OpenAIStage not given · Oct 22$30B
DeepSeekStage not given · Oct 22$15B

Recently funded

Topics in this field

language models llms · large language models · language models278 papers43 this week
retrieval augmented generation · knowledge graphs · retrieval augmented251 papers34 this week
memory construction · persistent memory · long term memory264 papers29 this week
large language models · machine translation · multilingual222 papers27 this week
large language models · language models · brain alignment148 papers18 this week
deep research · scientific literature · citations139 papers17 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.CLOct 6
Does Steering Break Your Model? A Multi-Dimensional Evaluation Suite for LLM Steering Methods
Haotian Yang, Huikang Jiang, Yucheng Wu, Wen-Jie Jiang, Chenpeng Wang +2
Activation steering provides a lightweight and flexible way to control large language model (LLM) behavior. However, effective steering requires more than inducing the intended behavior: it should also limit unintended changes and remain robust across inputs and training data. Existing evaluations cover these dimensions only in fragments. As a result, the trade-offs between efficacy and side effects have not been systematically characterized. We introduce SteerScope, a two-axis, multi-dimensional evaluation suite that jointly characterizes steering outcomes and method properties through 15 metrics. We score target efficacy and side effects on language quality, task capabilities, and safety and reliability, and further assess generalization and data dependence through steering-specific metrics for sample efficiency and sample sensitivity. Rather than comparing methods at a single operating point, we characterize the trade-offs between efficacy and side effects. Under matched models, tasks, and evaluation protocols, we benchmark 23 methods spanning 4 families, including prompting, LoRA, and SFT as baseline methods, and release the suite as an extensible codebase. We find that…
PDF arXiv
arXiv cs.CLOct 6
JevCascade: Readout Stability and Inference-Time Compute Allocation for Prefill-Only Decision Models
Ran Li, Lei Chen
We present JevCascade, an inference-time compute allocation framework for prefill-only decision models, built on readout stability and zero-label restricted readout prediction. Prefill-only decision models score every candidate in a menu in a single forward pass and never decode, which makes one call one to two orders of magnitude cheaper than a same-scale generative language model. We show that this read-out structure carries a testable property. When an intervention changes only the menu and leaves the input text fixed, the post-intervention accuracy is already determined by the cached first-pass distribution. The restriction predictor restricts the pass-1 probabilities to the menu, renormalizes and reads off the argmax, with no labels and no second pass. Across six model families, ten core datasets (sixteen in total) and two task types, menu-only interventions are predicted to within 4.2 points, exactly for one family. The property is ordinal, not probabilistic, since a probability-level variant of the same estimator errs by 21.0 points, and class-specific, since same-scale generative models err by 1.6 to 15.8 points and degrade as they grow. It makes inference-time compute…
PDF arXiv
arXiv cs.AIOct 6
Evidence Before Sampling: Interpretable Implicit Negative Candidate Discovery for Recommendation
Shreya Rajpal, Sonia Sharma, Swapnil Parekh, Lisa Li, Jeyendran Balakrishnan +2
Recommender systems learn from observed user-item interactions, but explicit negative feedback is often unavailable. Since deep learning models require negative signals for training, negative sampling methods typically treat selected unobserved interactions as negatives. However, a missing interaction does not explain why a user is uninterested in an item or whether there is sufficient evidence to label it negative. This is especially important in business recommendation, where negative signals should be interpretable and aligned with business objectives. We formulate implicit negative candidate discovery to identify unobserved interactions supported by observed customer behavior. We encode these patterns as symbolic rules, score them based on support, informativeness, and product relevance, and rank the retained rules by evidence. An LLM then interprets the retained rules using business objectives and domain knowledge; the interpretations are combined with the statistical evidence in the final report. We evaluate our method in an industrial B2B setting and across five public recommendation datasets. Candidate-quality evaluations in the industrial setting and three public…
PDF arXiv
arXiv cs.AIOct 6
AgentMemGate: Addressing Speculation Contamination in Conversational Assistant Memory
Chirag Sharma, Benjamin Fowlersmith, Karime Maamari
Conversational AI assistants with long-term memory extract facts from user messages into a store consulted in later conversations. A stated plan can enter that store as fact: a user who might move to Seattle may be recorded as already living there. We call this speculation contamination. Final-state memory benchmarks miss this error because they do not probe intermediate state and include few unresolved speculations. We present AgentMemGate, a write-time gate for profile-store memory that classifies extracted statements as speculation, completed event, correction, or other. Speculations remain outside memory, with conditions governing later promotion or deletion. We also contribute a dataset of multi-session conversations in which plans are confirmed, abandoned, or left unresolved. On our 147-conversation held-out set, Mem0 and Graphiti assert unresolved plans as current state for 35.2% and 27.3% of pending plans. On the core benchmark, AgentMemGate eliminates all observed contamination relative to the identical ungated pipeline (87.5% to zero for the most exposed extraction style) and raises task accuracy from 65% to 95%. On the harder held-out set, gated contamination is 3.4%…
PDF arXiv
arXiv cs.AIOct 6
On the Boundary of Admission Gates: An Injected-Truth Study of Falsification-First Selection in Quantitative Strategy Research
Tianlun Zheng
Strategy research conflates two problems: finding a profitable rule, and establishing that the finding is not search luck. The latter calls for admission gates -- statistical criteria that must be satisfied before a conclusion is adopted -- yet whether gates work, and at what cost, remains untested. We introduce an injected-truth protocol with a random-admission control that adopts at the same rate as the gate; only if the gate beats this control does it carry information rather than merely raise a threshold. Across synthetic and real-calibrated panels, gates eliminate false discoveries in the weak-signal regime but cut adoption to 1--7%, and add nothing when signals are strong. Most importantly, criteria computed on absolute rather than excess returns silently reject every candidate, including true signals. Keywords: multiple testing, backtest overfitting, strategy admission, injected-truth validation, excess returns, false discovery rate
PDF arXiv
arXiv cs.CLOct 6
Detecting LLM-Assisted Vietnamese Writing via Keystrokes under Behavioral Manipulation
Thanh Dong, An Ngo, Minh Dau, Rajesh Kumar
We study the robustness of keystroke dynamics for detecting large language model (LLM)-assisted writing. We introduce a Vietnamese keystroke dataset capturing realistic writing modes, including bona fide composition, transcription, and paraphrasing. We also define a behaviorally grounded threat model in which users deliberately alter typing patterns. To implement the threat model, we create behaviorally manipulated variants of the data designed to evade keystroke-based detection. We evaluate four keystroke modeling approaches: temporal and rhythmic representations, and sequential representations modeled with a one-dimensional convolutional neural network (1D-CNN) and TypeNet, under user-independent and context-independent settings. The results show that sequential models outperform feature-based approaches in most cases and that keystroke signals encode discriminative information about the writing process. However, detection is not uniformly robust: transcription is reliably identified, while paraphrasing and adversarially manipulated samples are frequently misclassified as bona fide when not explicitly modeled. To address this, we incorporate adversarial training using…
PDF arXiv

Papers

Top tools here

All tools

billion-context+259% downloads · 1.1M/wk+258.8%
jcode+233% downloads · 25.5k/wk+233.1%
TanStack AI+68% downloads · 1M/wk+67.7%
oh-my-claudecode+47% downloads · 7.7k/wk+47.4%
NVIDIA Dynamo+83% downloads · 18.4k/wk+83.4%

Recently funded

Market

Chaoqiumian TechnologySeed · Oct 26undisclosed
HypersphereSeed · Oct 26undisclosed
Star TherapeuticsSeries B+ · Oct 23$194.9M
OpenAIStage not given · Oct 22$30B
DeepSeekStage not given · Oct 22$15B

Topics in this field

language models llms · large language models · language models278 papers43 this week
retrieval augmented generation · knowledge graphs · retrieval augmented251 papers34 this week
memory construction · persistent memory · long term memory264 papers29 this week
large language models · machine translation · multilingual222 papers27 this week
large language models · language models · brain alignment148 papers18 this week
deep research · scientific literature · citations139 papers17 this week

Activity · 12 weeks

Open problems in Frontier LLMs

Evaluating and benchmarking decision models for reliability and calibration3 papers raise it · 4 address it
Decision models are increasingly used in LLM systems but lack standardized ways to measure correctness, confidence, and robustness across controlled variations.
- DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systemsraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
- Jeva: System One Models from First Principlesraises · addresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsaddresses
Efficient and accurate fact-checking of AI and human reviews1 paper raises it · 1 addresses it
Peer review systems struggle with factual errors in comments, and current methods are either too slow or inaccurate for large-scale use.
- PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviewsraises · addresses
Long-horizon agent context management and memory retention2 papers raise it · 3 address it
Agents lose critical information when context windows are exceeded, and current compaction methods fail to preserve decision-relevant details.
- REMORY: Learning Residual Memory for Context Compactionraises · addresses
- Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agentsraises · addresses
- Cortex: Consolidating Context into Parametric Memoryaddresses
Real-world web-based deep search and evidence integration1 paper raises it · 1 addresses it
Agents cannot reliably perform multi-step research in live, open web environments with diverse, interactive, and heterogeneous information sources.
- Deep Search in Real-World Web Environmentsraises · addresses
User behavior modeling for long-term personalization1 paper raises it · 1 addresses it
LLMs lack the ability to infer and adapt to evolving user preferences and intent from real-world behavior data over time.
- Vela: User Behavior as Supervision Sparks LLM Intelligenceraises · addresses

Named by an AI from these papers' abstracts.

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.CLOct 6
Does Steering Break Your Model? A Multi-Dimensional Evaluation Suite for LLM Steering Methods
Haotian Yang, Huikang Jiang, Yucheng Wu, Wen-Jie Jiang, Chenpeng Wang +2
Activation steering provides a lightweight and flexible way to control large language model (LLM) behavior. However, effective steering requires more than inducing the intended behavior: it should also limit unintended changes and remain robust across inputs and training data. Existing evaluations cover these dimensions only in fragments. As a result, the trade-offs between efficacy and side effects have not been systematically characterized. We introduce SteerScope, a two-axis, multi-dimensional evaluation suite that jointly characterizes steering outcomes and method properties through 15 metrics. We score target efficacy and side effects on language quality, task capabilities, and safety and reliability, and further assess generalization and data dependence through steering-specific metrics for sample efficiency and sample sensitivity. Rather than comparing methods at a single operating point, we characterize the trade-offs between efficacy and side effects. Under matched models, tasks, and evaluation protocols, we benchmark 23 methods spanning 4 families, including prompting, LoRA, and SFT as baseline methods, and release the suite as an extensible codebase. We find that…
PDF arXiv
arXiv cs.CLOct 6
JevCascade: Readout Stability and Inference-Time Compute Allocation for Prefill-Only Decision Models
Ran Li, Lei Chen
We present JevCascade, an inference-time compute allocation framework for prefill-only decision models, built on readout stability and zero-label restricted readout prediction. Prefill-only decision models score every candidate in a menu in a single forward pass and never decode, which makes one call one to two orders of magnitude cheaper than a same-scale generative language model. We show that this read-out structure carries a testable property. When an intervention changes only the menu and leaves the input text fixed, the post-intervention accuracy is already determined by the cached first-pass distribution. The restriction predictor restricts the pass-1 probabilities to the menu, renormalizes and reads off the argmax, with no labels and no second pass. Across six model families, ten core datasets (sixteen in total) and two task types, menu-only interventions are predicted to within 4.2 points, exactly for one family. The property is ordinal, not probabilistic, since a probability-level variant of the same estimator errs by 21.0 points, and class-specific, since same-scale generative models err by 1.6 to 15.8 points and degrade as they grow. It makes inference-time compute…
PDF arXiv
arXiv cs.AIOct 6
Evidence Before Sampling: Interpretable Implicit Negative Candidate Discovery for Recommendation
Shreya Rajpal, Sonia Sharma, Swapnil Parekh, Lisa Li, Jeyendran Balakrishnan +2
Recommender systems learn from observed user-item interactions, but explicit negative feedback is often unavailable. Since deep learning models require negative signals for training, negative sampling methods typically treat selected unobserved interactions as negatives. However, a missing interaction does not explain why a user is uninterested in an item or whether there is sufficient evidence to label it negative. This is especially important in business recommendation, where negative signals should be interpretable and aligned with business objectives. We formulate implicit negative candidate discovery to identify unobserved interactions supported by observed customer behavior. We encode these patterns as symbolic rules, score them based on support, informativeness, and product relevance, and rank the retained rules by evidence. An LLM then interprets the retained rules using business objectives and domain knowledge; the interpretations are combined with the statistical evidence in the final report. We evaluate our method in an industrial B2B setting and across five public recommendation datasets. Candidate-quality evaluations in the industrial setting and three public…
PDF arXiv
arXiv cs.AIOct 6
AgentMemGate: Addressing Speculation Contamination in Conversational Assistant Memory
Chirag Sharma, Benjamin Fowlersmith, Karime Maamari
Conversational AI assistants with long-term memory extract facts from user messages into a store consulted in later conversations. A stated plan can enter that store as fact: a user who might move to Seattle may be recorded as already living there. We call this speculation contamination. Final-state memory benchmarks miss this error because they do not probe intermediate state and include few unresolved speculations. We present AgentMemGate, a write-time gate for profile-store memory that classifies extracted statements as speculation, completed event, correction, or other. Speculations remain outside memory, with conditions governing later promotion or deletion. We also contribute a dataset of multi-session conversations in which plans are confirmed, abandoned, or left unresolved. On our 147-conversation held-out set, Mem0 and Graphiti assert unresolved plans as current state for 35.2% and 27.3% of pending plans. On the core benchmark, AgentMemGate eliminates all observed contamination relative to the identical ungated pipeline (87.5% to zero for the most exposed extraction style) and raises task accuracy from 65% to 95%. On the harder held-out set, gated contamination is 3.4%…
PDF arXiv
arXiv cs.AIOct 6
On the Boundary of Admission Gates: An Injected-Truth Study of Falsification-First Selection in Quantitative Strategy Research
Tianlun Zheng
Strategy research conflates two problems: finding a profitable rule, and establishing that the finding is not search luck. The latter calls for admission gates -- statistical criteria that must be satisfied before a conclusion is adopted -- yet whether gates work, and at what cost, remains untested. We introduce an injected-truth protocol with a random-admission control that adopts at the same rate as the gate; only if the gate beats this control does it carry information rather than merely raise a threshold. Across synthetic and real-calibrated panels, gates eliminate false discoveries in the weak-signal regime but cut adoption to 1--7%, and add nothing when signals are strong. Most importantly, criteria computed on absolute rather than excess returns silently reject every candidate, including true signals. Keywords: multiple testing, backtest overfitting, strategy admission, injected-truth validation, excess returns, false discovery rate
PDF arXiv
arXiv cs.CLOct 6
Detecting LLM-Assisted Vietnamese Writing via Keystrokes under Behavioral Manipulation
Thanh Dong, An Ngo, Minh Dau, Rajesh Kumar
We study the robustness of keystroke dynamics for detecting large language model (LLM)-assisted writing. We introduce a Vietnamese keystroke dataset capturing realistic writing modes, including bona fide composition, transcription, and paraphrasing. We also define a behaviorally grounded threat model in which users deliberately alter typing patterns. To implement the threat model, we create behaviorally manipulated variants of the data designed to evade keystroke-based detection. We evaluate four keystroke modeling approaches: temporal and rhythmic representations, and sequential representations modeled with a one-dimensional convolutional neural network (1D-CNN) and TypeNet, under user-independent and context-independent settings. The results show that sequential models outperform feature-based approaches in most cases and that keystroke signals encode discriminative information about the writing process. However, detection is not uniformly robust: transcription is reliably identified, while paraphrasing and adversarially manipulated samples are frequently misclassified as bona fide when not explicitly modeled. To address this, we incorporate adversarial training using…
PDF arXiv

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