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Open problems in Inference & serving

Efficient long-horizon context management3 papers raise it · 3 address it
Long-horizon agents need to retain and use information beyond finite context windows without losing critical details.
- REMORY: Learning Residual Memory for Context Compactionraises · addresses
- Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agentsraises · addresses
- MemT-1.0: Towards Memory-Native Evolutionraises · addresses
Length generalization in state tracking1 paper raises it · 1 addresses it
Models trained on short sequences often fail to generalize to much longer sequences, limiting their practical use.
- PAR: Progress-Aligned Recurrence for Length Generalization in State Trackingraises · addresses
Token-level routing efficiency in serving systems1 paper raises it · 1 addresses it
Fine-grained token-level routing improves cost-quality trade-offs but introduces synchronization and complexity challenges in serving.
- TokenRouter: Efficient Serving System for Token-Level LLM Routingraises · addresses
Memory-induced sycophancy in long-term agents1 paper raises it · 1 addresses it
Persistent memories can cause agents to over-align with user beliefs, even when those beliefs are incorrect or outdated.
- Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancyraises · addresses
Sparse attention computation overhead1 paper raises it · 1 addresses it
Sparse attention reduces compute but still requires scoring all keys, limiting efficiency gains.
- DoSA: Dots Sparse Attentionraises · addresses

Named by an AI from these papers' abstracts.

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TrimagnetixSeed · Oct 15$1M

Recently funded

Topics in this field

agent memory · memory construction · term memory262 papers52 this week
diffusion language models · language models diffusion · models diffusion language157 papers34 this week
gpus · gpu · throughput227 papers33 this week
continual learning · catastrophic forgetting · forgetting244 papers28 this week
speculative decoding accelerates · self speculative decoding · speculative decoding92 papers23 this week
looped language models · recurrent depth · loops95 papers22 this week

Topics in this field

Active contributors · 90 days

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Activity · 12 weeks

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Recent papers

All papers in Research

arXiv cs.AROct 6
CACHEFORGE: LLM-Guided End-to-End Generative Cache Replacement Policy for Performance and Hardware Efficiency
Kaushal Mhapsekar, Bita Aslrousta, Brijesh Kumar Bhayana, Paula Contreras, Azam Ghanbari +3
Modern cache replacement designs saturate because they operate within fixed representational structures, hand-crafted and heuristic based feature-engineered predictors, or offline imitation models that cannot generate new decision logic on their own. At the same time, replacement is shaped by the causal interaction of prefetching, thrashing, spatial locality, and access-type behavior, producing an enormous design space that is difficult to traverse manually. Prior approaches typically rely on heuristics, parameter tuning, or imitation of an offline optimal policy, capturing correlations rather than synthesizing new mechanisms. As a result, their performance gains often plateau and they overfit under dynamic workload conditions. CACHEFORGE is the first framework to evolve cache-replacement policies end-to-end by embedding a large language model inside a governed hardware-aware loop. In each iteration, the LLM proposes new C++ replacement logic, the policy is evaluated under a trace-based CRC-2 ChampSim simulator, and the framework enforces feasibility through reward shaping, structural checks, dynamic mutation, temperature scheduling, and cross-policy crossover. This closed-loop…
PDF arXiv
arXiv cs.CLCodeOct 6▲ 15
DLoop: Looped Speculative Decoding
Geonmo Gu, Byeongho Heo, HeeJae Jun, Yoohoon Kang, Sangmin Lee +2
Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens that the target model subsequently verifies. With increasingly capable draft models, we find that the target model frequently accepts all tokens produced in a drafting stage. A verification nevertheless follows each drafting stage, resulting in unnecessary target-model forward passes even when drafting could have continued. Adaptive draft length methods decide during decoding how many draft tokens precede a verification, but they raise the speedup only for autoregressive draft models. For a parallel draft model, drafting further requires target-model hidden states for draft tokens that have not been verified. We propose DLoop, a looped form of speculative decoding that adaptively performs multiple drafting stages before verification. DLoop continues drafting while the draft model remains confident and verifies all accumulated draft tokens together. Loop-aware training keeps the draft model reliable in the additional drafting stages by exposing it to its own hidden states for unverified draft tokens. By spending additional…
PDF arXiv Code
arXiv cs.CLOct 6
Monte Carlo Estimation for KV Cache Eviction
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Wajih Hassan Raza, Atta Ul Asad +3
Most KV-cache eviction methods ask, in effect, which memory appeared important while reading the prompt? We instead ask which memory will matter while answering? Since decoding queries are unavailable at eviction time, prior future-aware methods rely on pseudo-responses or synthetic future-query estimates. We cast fixed-budget future-aware eviction as distributional estimation over plausible model-conditional query trajectories and introduce LORE-KV (Lookahead Output-perturbation with Reliability-weighted Ensembles for Key-Value caches), a training-free method that samples short autoregressive continuations from the frozen target model and uses their response-side query states to estimate prompt-token utility. Tokens are scored by projected leave-one-out attention-output deletion cost and aggregated across sampled futures with optional trajectory weighting. The temporary continuations are discarded before final decoding, requiring no auxiliary model or training. Ablations isolate the mechanism: at B=128, a single response-side continuation recovers about 89% of the gain over the prompt-window control, while additional futures provide smaller improvements. At B=128, LORE-KV raises…
PDF arXiv
arXiv cs.AIOct 6
VALSE: Vertical Adaptive Layer Skipping for Efficient Inference in Large Language Models
Jia-Dong Zhang
This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (theorem 2) giving a closed-form expression for the computational cost of arbitrary per-sample skip schedules as a function of layer-wise skip probabilities; (ii) function-space superset (theorem 10) and strict inclusion (theorem 11) theorems showing that skip-layer models are strictly contained in---yet meaningfully approximate---the full-layer function space, with an explicit separating example; and (iii) a structural duality between VALSE and Mixture-of-Experts architectures (proposition 6), positioning vertical depth-wise sparsity as the orthogonal counterpart to horizontal width-wise sparsity. Building on this theory, we propose VALSE (Vertical Adaptive Layer Skipping for Efficiency), a per-sample, non-contiguous layer skipping method: a lightweight difficulty estimator scores each input from the first few layers, and per-layer gates selectively skip redundant layers---including arbitrary middle layers while retaining deeper ones---so that only the necessary depth is activated for each input, whose feasibility is preliminarily…
PDF arXiv
arXiv cs.CLCodeOct 6▲ 33
Recurrent Looped Transformer
Yifan Zhang, Jichen Feng, Shihan Qin, Sun-Yuan Kung, Mengdi Wang
State tracking requires an update at every input, but the depth a Transformer applies to each token is fixed regardless of sequence length. We introduce the Recurrent Looped Transformer (RLT), which splits its layers between a parallel causal encoder and a recurrent decoder. At each token, the decoder merges the encoder output with the previous token's final decoder state, so the computation path grows with sequence length at a fixed per-token cost. On six algorithmic tasks, we compare five splits of eight layers with an eight-layer Transformer over three seeds. Trained on at most 40 bits, two RLT splits generalize parity to 256 bits with 100% accuracy in every seed, while the Transformer stays at chance. On swap-based $S_5$ permutation tracking at eight times the training length, RLT reaches 97% final-state accuracy versus under 1% for the Transformer, and accuracy increases with decoder depth. On modular arithmetic beyond the training lengths, RLT reaches up to 93% versus 33\% for the Transformer. Ablations show that these gains depend on the feedback: removing it drops parity and swap-based $S_5$ to chance at every split. Updating the feedback once per four-token chunk lets…
PDF arXiv Code
arXiv cs.LGOct 6
TAFFY: A Task-Adaptive Tabular Foundation Model with In-Context Diversity
Zijian Li, Xiangchen Song, Gongxu Luo, Jie Qiao, Ruichu Cai +5
Recent progress in tabular foundation models suggests that training on synthetic tasks can substantially improve in-context learning capabilities, with overall performance largely depending on how well models can infer task-specific predictive relationships from the available context during inference. In this paper, we introduce TAFFY, a tabular foundation model with an In-Context Diversity Prior and a Task-Conditioned Looped Transformer that strengthen this ability. Specifically, to construct each synthetic pretraining context, the In-Context Diversity Prior samples from multiple related environments derived via controlled interventions and distribution shifts on a shared causal process. This in-context diversity encourages the model to learn a more comprehensive and task-specific representation. Moreover, the Task-Conditioned Looped Transformer iteratively and selectively applies a shared group of Transformer blocks to refine contextual representations, with a task-conditioned gate modulating the final hidden-state update. This enables task-adaptive iterative refinement. Together, these components encourage the model to identify predictive relationships from contextual…
PDF arXiv

Papers

Top tools here

All tools

NVIDIA Dynamo+62% downloads · 16k/wk+62.3%
BentoML+40% downloads · 44.6k/wk+39.6%
mlx-lm1 release this week
MLX+26% downloads · 349.8k/wk+26.5%
vLLM1 release this week

Topics in this field

agent memory · memory construction · term memory262 papers52 this week
diffusion language models · language models diffusion · models diffusion language157 papers34 this week
gpus · gpu · throughput227 papers33 this week
continual learning · catastrophic forgetting · forgetting244 papers28 this week
speculative decoding accelerates · self speculative decoding · speculative decoding92 papers23 this week
looped language models · recurrent depth · loops95 papers22 this week

Active contributors · 90 days

Recently funded

Market

Liufang TechnologySeries B+ · Oct 17$15M
EMode PhotonixPre-seed · Oct 16$1M
XHEARTSeries B+ · Oct 16undisclosed
MueonSeries A · Oct 15$65M
TrimagnetixSeed · Oct 15$1M

Recent papers

All papers in Research

arXiv cs.AROct 6
CACHEFORGE: LLM-Guided End-to-End Generative Cache Replacement Policy for Performance and Hardware Efficiency
Kaushal Mhapsekar, Bita Aslrousta, Brijesh Kumar Bhayana, Paula Contreras, Azam Ghanbari +3
Modern cache replacement designs saturate because they operate within fixed representational structures, hand-crafted and heuristic based feature-engineered predictors, or offline imitation models that cannot generate new decision logic on their own. At the same time, replacement is shaped by the causal interaction of prefetching, thrashing, spatial locality, and access-type behavior, producing an enormous design space that is difficult to traverse manually. Prior approaches typically rely on heuristics, parameter tuning, or imitation of an offline optimal policy, capturing correlations rather than synthesizing new mechanisms. As a result, their performance gains often plateau and they overfit under dynamic workload conditions. CACHEFORGE is the first framework to evolve cache-replacement policies end-to-end by embedding a large language model inside a governed hardware-aware loop. In each iteration, the LLM proposes new C++ replacement logic, the policy is evaluated under a trace-based CRC-2 ChampSim simulator, and the framework enforces feasibility through reward shaping, structural checks, dynamic mutation, temperature scheduling, and cross-policy crossover. This closed-loop…
PDF arXiv
arXiv cs.CLCodeOct 6▲ 15
DLoop: Looped Speculative Decoding
Geonmo Gu, Byeongho Heo, HeeJae Jun, Yoohoon Kang, Sangmin Lee +2
Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens that the target model subsequently verifies. With increasingly capable draft models, we find that the target model frequently accepts all tokens produced in a drafting stage. A verification nevertheless follows each drafting stage, resulting in unnecessary target-model forward passes even when drafting could have continued. Adaptive draft length methods decide during decoding how many draft tokens precede a verification, but they raise the speedup only for autoregressive draft models. For a parallel draft model, drafting further requires target-model hidden states for draft tokens that have not been verified. We propose DLoop, a looped form of speculative decoding that adaptively performs multiple drafting stages before verification. DLoop continues drafting while the draft model remains confident and verifies all accumulated draft tokens together. Loop-aware training keeps the draft model reliable in the additional drafting stages by exposing it to its own hidden states for unverified draft tokens. By spending additional…
PDF arXiv Code
arXiv cs.CLOct 6
Monte Carlo Estimation for KV Cache Eviction
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Wajih Hassan Raza, Atta Ul Asad +3
Most KV-cache eviction methods ask, in effect, which memory appeared important while reading the prompt? We instead ask which memory will matter while answering? Since decoding queries are unavailable at eviction time, prior future-aware methods rely on pseudo-responses or synthetic future-query estimates. We cast fixed-budget future-aware eviction as distributional estimation over plausible model-conditional query trajectories and introduce LORE-KV (Lookahead Output-perturbation with Reliability-weighted Ensembles for Key-Value caches), a training-free method that samples short autoregressive continuations from the frozen target model and uses their response-side query states to estimate prompt-token utility. Tokens are scored by projected leave-one-out attention-output deletion cost and aggregated across sampled futures with optional trajectory weighting. The temporary continuations are discarded before final decoding, requiring no auxiliary model or training. Ablations isolate the mechanism: at B=128, a single response-side continuation recovers about 89% of the gain over the prompt-window control, while additional futures provide smaller improvements. At B=128, LORE-KV raises…
PDF arXiv
arXiv cs.AIOct 6
VALSE: Vertical Adaptive Layer Skipping for Efficient Inference in Large Language Models
Jia-Dong Zhang
This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (theorem 2) giving a closed-form expression for the computational cost of arbitrary per-sample skip schedules as a function of layer-wise skip probabilities; (ii) function-space superset (theorem 10) and strict inclusion (theorem 11) theorems showing that skip-layer models are strictly contained in---yet meaningfully approximate---the full-layer function space, with an explicit separating example; and (iii) a structural duality between VALSE and Mixture-of-Experts architectures (proposition 6), positioning vertical depth-wise sparsity as the orthogonal counterpart to horizontal width-wise sparsity. Building on this theory, we propose VALSE (Vertical Adaptive Layer Skipping for Efficiency), a per-sample, non-contiguous layer skipping method: a lightweight difficulty estimator scores each input from the first few layers, and per-layer gates selectively skip redundant layers---including arbitrary middle layers while retaining deeper ones---so that only the necessary depth is activated for each input, whose feasibility is preliminarily…
PDF arXiv
arXiv cs.CLCodeOct 6▲ 33
Recurrent Looped Transformer
Yifan Zhang, Jichen Feng, Shihan Qin, Sun-Yuan Kung, Mengdi Wang
State tracking requires an update at every input, but the depth a Transformer applies to each token is fixed regardless of sequence length. We introduce the Recurrent Looped Transformer (RLT), which splits its layers between a parallel causal encoder and a recurrent decoder. At each token, the decoder merges the encoder output with the previous token's final decoder state, so the computation path grows with sequence length at a fixed per-token cost. On six algorithmic tasks, we compare five splits of eight layers with an eight-layer Transformer over three seeds. Trained on at most 40 bits, two RLT splits generalize parity to 256 bits with 100% accuracy in every seed, while the Transformer stays at chance. On swap-based $S_5$ permutation tracking at eight times the training length, RLT reaches 97% final-state accuracy versus under 1% for the Transformer, and accuracy increases with decoder depth. On modular arithmetic beyond the training lengths, RLT reaches up to 93% versus 33\% for the Transformer. Ablations show that these gains depend on the feedback: removing it drops parity and swap-based $S_5$ to chance at every split. Updating the feedback once per four-token chunk lets…
PDF arXiv Code
arXiv cs.LGOct 6
TAFFY: A Task-Adaptive Tabular Foundation Model with In-Context Diversity
Zijian Li, Xiangchen Song, Gongxu Luo, Jie Qiao, Ruichu Cai +5
Recent progress in tabular foundation models suggests that training on synthetic tasks can substantially improve in-context learning capabilities, with overall performance largely depending on how well models can infer task-specific predictive relationships from the available context during inference. In this paper, we introduce TAFFY, a tabular foundation model with an In-Context Diversity Prior and a Task-Conditioned Looped Transformer that strengthen this ability. Specifically, to construct each synthetic pretraining context, the In-Context Diversity Prior samples from multiple related environments derived via controlled interventions and distribution shifts on a shared causal process. This in-context diversity encourages the model to learn a more comprehensive and task-specific representation. Moreover, the Task-Conditioned Looped Transformer iteratively and selectively applies a shared group of Transformer blocks to refine contextual representations, with a task-conditioned gate modulating the final hidden-state update. This enables task-adaptive iterative refinement. Together, these components encourage the model to identify predictive relationships from contextual…
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 1h ago

Inference & serving

Making models fast and cheap to run: serving systems, kernels, KV-cache, speculative decoding, hardware.

269papers this week−10.0% vs last week

All papers · 1.6k With code · 63

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1.5k papers this month

Open problems in Inference & serving

Efficient long-horizon context management3 papers raise it · 3 address it
Long-horizon agents need to retain and use information beyond finite context windows without losing critical details.
- REMORY: Learning Residual Memory for Context Compactionraises · addresses
- Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agentsraises · addresses
- MemT-1.0: Towards Memory-Native Evolutionraises · addresses
Length generalization in state tracking1 paper raises it · 1 addresses it
Models trained on short sequences often fail to generalize to much longer sequences, limiting their practical use.
- PAR: Progress-Aligned Recurrence for Length Generalization in State Trackingraises · addresses
Token-level routing efficiency in serving systems1 paper raises it · 1 addresses it
Fine-grained token-level routing improves cost-quality trade-offs but introduces synchronization and complexity challenges in serving.
- TokenRouter: Efficient Serving System for Token-Level LLM Routingraises · addresses
Memory-induced sycophancy in long-term agents1 paper raises it · 1 addresses it
Persistent memories can cause agents to over-align with user beliefs, even when those beliefs are incorrect or outdated.
- Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancyraises · addresses
Sparse attention computation overhead1 paper raises it · 1 addresses it
Sparse attention reduces compute but still requires scoring all keys, limiting efficiency gains.
- DoSA: Dots Sparse Attentionraises · addresses

Named by an AI from these papers' abstracts.