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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 relevant information without exceeding finite context windows.
- 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
Token-level LLM routing efficiency1 paper raises it · 1 addresses it
Fine-grained token-level routing improves cost-quality trade-offs but strains existing serving systems.
- TokenRouter: Efficient Serving System for Token-Level LLM Routingraises · addresses
Memory-induced sycophancy in agents1 paper raises it · 1 addresses it
Persistent memories can cause agents to over-align with user beliefs, even when they are incorrect or outdated.
- Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancyraises · addresses
Quality gap in parallel token generation2 papers raise it · 2 address it
Diffusion language models generate tokens in parallel but lag behind autoregressive models in quality.
- ALoDLM: Adaptively Looped Diffusion Language Modelsraises · addresses
- Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Modelsraises · addresses
Sparse attention computation overhead1 paper raises it · 1 addresses it
Sparse attention reduces computation but still requires scoring all keys, limiting efficiency gains.
- DoSA: Dots Sparse Attentionraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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EMode PhotonixPre-seed · Oct 16$1M
MueonSeries A · Oct 15$65M
TrimagnetixSeed · Oct 15$1M
ModalSeries B+ · Oct 14$750M
Salience LabsSeries B+ · Oct 14$125M

Recently funded

Topics in this field

agent memory · memory construction · term memory248 papers53 this week
gpus · gpu · throughput212 papers32 this week
diffusion language models · language models diffusion · models diffusion language145 papers27 this week
continual learning · catastrophic forgetting · forgetting230 papers21 this week
sparse attention · linear attention · attention layers131 papers21 this week
aware routing · model routing · model llm routing82 papers19 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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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.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.CRCodeOct 5▲ 2
Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents
Venkata M Sangaraju, Sudhir Vissa
Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester could not derive, and departments can silently compute a same-named key performance indicator (KPI) through conflicting logic. Existing agent-memory systems (e.g., MemGPT, Zep, A-MEM) gate retrieval by content, ownership, and role, not derivation, missing a cached insight that embeds a forbidden column. We introduce the Analytical Memory Unit (AMU), a memory schema that attaches a full derivation (lineage) graph to every cached result, gated by a retrieval policy that serves a hit only when the requester is authorised for every column touched. Provided lineage recording is complete, we prove by construction that the policy blocks retrieval of results derived from a sensitive column outside the requester's permissions, at O(n) worst case -- a conditional design guarantee, not an empirical claim, that excludes derived features encoding sensitive information without naming their source. Eliminating measured leakage required 75-90% recorded lineage completeness, so we treat 90% as a conservative deployment target. Across six…
PDF arXiv Code
arXiv cs.LGCodeOct 5▲ 8
Distributionally Robust Mixture-of-Experts Training
Xin Teng, Muxiao Li, Hongyi Wen
Mixture-of-Experts (MoE) transformers scale capacity by activating only a few experts per token, but this sparsity creates a hidden reliability problem: when routing is imperfect, load-balanced models may send tokens to experts that are insufficiently trained for the assigned inputs. We propose Distributionally Robust MoE Training (DRMoET), a drop-in objective that treats layer-wise experts as endogenous robustness groups and optimizes high-loss routing outcomes rather than merely equalizing traffic. DRMoET updates a per-layer expert distribution by an entropy-regularized softmax rule on EMA-smoothed, activation-weighted expert losses, strengthening plausible non-top routing paths while preserving standard MoE computation. Under the FLAME-MoE recipe at 746M-total and 10.3B-total scales, DRMoET improves downstream averages over both standard FLAME-MoE and auxiliary-loss-free balancing. At 10.3B total parameters and 67B training tokens, DRMoET improves the seven-task average from 0.6625 to 0.6767, while the auxiliary-loss-free baseline achieves 0.6431. Mechanistic analyses show lower expert-loss variance with nearly unchanged mean loss, 4.3% lower excess loss under forced mid-$k$…
PDF arXiv Code
arXiv cs.LGCodeOct 5▲ 35
Towards Looped Models Done Right, Part II: Rethinking at Fixed Points
Benhao Huang, Chufan Shi, Junlin Chen, Shicheng Wen, Zhengzhong Liu +2
Every recurrence of a looped language model adds cost in training, decoding, prefill, and reinforcement learning (RL). The closer recurrent states get to fixed points, the less the path to them matters. This enables truncated backpropagation in training; terminal key-value (KV) sharing for decoding with almost no loss in accuracy; a distilled student that prefills up to 1.79x faster; and RL updates that compute gradients from saved rollout states, 2x faster than backpropagating through the replayed trajectory. We therefore improve the two components of training that shape these fixed points: the depth prior and input injection. Fixed-depth training breaks KV sharing, and Huginn's broad depth prior supports sharing but dilutes supervision at the target depth more than sharing requires; we learn the prior from prediction feedback, with an entropy term that keeps it broad. Existing injection schemes let the state's component along the input amplify or cancel the injection; we remove this component with orthogonal injection. From 100M to 1.6B parameters, the learned prior and orthogonal injection lower perplexity at every scale relative to Huginn's prior and existing injection…
PDF arXiv Code
arXiv cs.CLCodeOct 5▲ 25
MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents
Haozhen Zhang, Haodong Yue, Quanyu Long, Jianzhu Bao, Qingyuan Liu +4
Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent memory systems construct memory in a query-agnostic manner, which can incur unnecessary preprocessing cost and discard details that later prove essential. Recent studies have begun shifting memory processing toward runtime adaptation, but typically specialize in particular operations or fixed processing schemes, leaving flexible control over performance, cost, and latency largely underexplored. To address this challenge, we present \textbf{MemPilot}, a flexible framework that orchestrates on-demand memory curation under different performance--cost--latency preferences. Specifically, we optimize a multi-step LLM policy via reinforcement learning to iteratively choose between retrieving from query-agnostic memory and delegating query-specific curation of raw multimodal history to heterogeneous LLMs and VLMs. The policy jointly controls evidence amount, curation instructions, model selection, and visual access, enabling fine-grained allocation of runtime computation. To optimize this policy under competing objectives, we adapt…
PDF arXiv Code

Papers

Recently funded

Market

EMode PhotonixPre-seed · Oct 16$1M
MueonSeries A · Oct 15$65M
TrimagnetixSeed · Oct 15$1M
ModalSeries B+ · Oct 14$750M
Salience LabsSeries B+ · Oct 14$125M

Topics in this field

agent memory · memory construction · term memory248 papers53 this week
gpus · gpu · throughput212 papers32 this week
diffusion language models · language models diffusion · models diffusion language145 papers27 this week
continual learning · catastrophic forgetting · forgetting230 papers21 this week
sparse attention · linear attention · attention layers131 papers21 this week
aware routing · model routing · model llm routing82 papers19 this week

Active contributors · 90 days

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 16m ago

Inference & serving

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

244papers this week−19.2% vs last week

All papers · 1.5k With code · 60

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1.4k papers this month

Recent papers

All in Research →

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.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.CRCodeOct 5▲ 2
Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents
Venkata M Sangaraju, Sudhir Vissa
Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester could not derive, and departments can silently compute a same-named key performance indicator (KPI) through conflicting logic. Existing agent-memory systems (e.g., MemGPT, Zep, A-MEM) gate retrieval by content, ownership, and role, not derivation, missing a cached insight that embeds a forbidden column. We introduce the Analytical Memory Unit (AMU), a memory schema that attaches a full derivation (lineage) graph to every cached result, gated by a retrieval policy that serves a hit only when the requester is authorised for every column touched. Provided lineage recording is complete, we prove by construction that the policy blocks retrieval of results derived from a sensitive column outside the requester's permissions, at O(n) worst case -- a conditional design guarantee, not an empirical claim, that excludes derived features encoding sensitive information without naming their source. Eliminating measured leakage required 75-90% recorded lineage completeness, so we treat 90% as a conservative deployment target. Across six…
PDF arXiv Code
arXiv cs.LGCodeOct 5▲ 8
Distributionally Robust Mixture-of-Experts Training
Xin Teng, Muxiao Li, Hongyi Wen
Mixture-of-Experts (MoE) transformers scale capacity by activating only a few experts per token, but this sparsity creates a hidden reliability problem: when routing is imperfect, load-balanced models may send tokens to experts that are insufficiently trained for the assigned inputs. We propose Distributionally Robust MoE Training (DRMoET), a drop-in objective that treats layer-wise experts as endogenous robustness groups and optimizes high-loss routing outcomes rather than merely equalizing traffic. DRMoET updates a per-layer expert distribution by an entropy-regularized softmax rule on EMA-smoothed, activation-weighted expert losses, strengthening plausible non-top routing paths while preserving standard MoE computation. Under the FLAME-MoE recipe at 746M-total and 10.3B-total scales, DRMoET improves downstream averages over both standard FLAME-MoE and auxiliary-loss-free balancing. At 10.3B total parameters and 67B training tokens, DRMoET improves the seven-task average from 0.6625 to 0.6767, while the auxiliary-loss-free baseline achieves 0.6431. Mechanistic analyses show lower expert-loss variance with nearly unchanged mean loss, 4.3% lower excess loss under forced mid-$k$…
PDF arXiv Code
arXiv cs.LGCodeOct 5▲ 35
Towards Looped Models Done Right, Part II: Rethinking at Fixed Points
Benhao Huang, Chufan Shi, Junlin Chen, Shicheng Wen, Zhengzhong Liu +2
Every recurrence of a looped language model adds cost in training, decoding, prefill, and reinforcement learning (RL). The closer recurrent states get to fixed points, the less the path to them matters. This enables truncated backpropagation in training; terminal key-value (KV) sharing for decoding with almost no loss in accuracy; a distilled student that prefills up to 1.79x faster; and RL updates that compute gradients from saved rollout states, 2x faster than backpropagating through the replayed trajectory. We therefore improve the two components of training that shape these fixed points: the depth prior and input injection. Fixed-depth training breaks KV sharing, and Huginn's broad depth prior supports sharing but dilutes supervision at the target depth more than sharing requires; we learn the prior from prediction feedback, with an entropy term that keeps it broad. Existing injection schemes let the state's component along the input amplify or cancel the injection; we remove this component with orthogonal injection. From 100M to 1.6B parameters, the learned prior and orthogonal injection lower perplexity at every scale relative to Huginn's prior and existing injection…
PDF arXiv Code
arXiv cs.CLCodeOct 5▲ 25
MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents
Haozhen Zhang, Haodong Yue, Quanyu Long, Jianzhu Bao, Qingyuan Liu +4
Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent memory systems construct memory in a query-agnostic manner, which can incur unnecessary preprocessing cost and discard details that later prove essential. Recent studies have begun shifting memory processing toward runtime adaptation, but typically specialize in particular operations or fixed processing schemes, leaving flexible control over performance, cost, and latency largely underexplored. To address this challenge, we present \textbf{MemPilot}, a flexible framework that orchestrates on-demand memory curation under different performance--cost--latency preferences. Specifically, we optimize a multi-step LLM policy via reinforcement learning to iteratively choose between retrieving from query-agnostic memory and delegating query-specific curation of raw multimodal history to heterogeneous LLMs and VLMs. The policy jointly controls evidence amount, curation instructions, model selection, and visual access, enabling fine-grained allocation of runtime computation. To optimize this policy under competing objectives, we adapt…
PDF arXiv Code

Open problems in Inference & serving

Efficient long-horizon context management3 papers raise it · 3 address it
Long-horizon agents need to retain and use relevant information without exceeding finite context windows.
- 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
Token-level LLM routing efficiency1 paper raises it · 1 addresses it
Fine-grained token-level routing improves cost-quality trade-offs but strains existing serving systems.
- TokenRouter: Efficient Serving System for Token-Level LLM Routingraises · addresses
Memory-induced sycophancy in agents1 paper raises it · 1 addresses it
Persistent memories can cause agents to over-align with user beliefs, even when they are incorrect or outdated.
- Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancyraises · addresses
Quality gap in parallel token generation2 papers raise it · 2 address it
Diffusion language models generate tokens in parallel but lag behind autoregressive models in quality.
- ALoDLM: Adaptively Looped Diffusion Language Modelsraises · addresses
- Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Modelsraises · addresses
Sparse attention computation overhead1 paper raises it · 1 addresses it
Sparse attention reduces computation but still requires scoring all keys, limiting efficiency gains.
- DoSA: Dots Sparse Attentionraises · addresses

Named by an AI from these papers' abstracts.

Top tools here

Coming with more sources: stars history

All tools

NVIDIA Dynamo+60% downloads · 15.8k/wk+60.0%
BentoML+30% downloads · 41k/wk+29.7%
MLX+26% downloads · 342.6k/wk+25.5%
Open WebUI3 releases this week
mlx-lm+8% downloads · 140.9k/wk+7.5%