KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning
Kun Feng, Ziwei Shan, Yuchen Fang, Yiyang Tan, Sihan Lu, Shuqi Gu, Lintao Ma, Xingyu Lu
Abstract
Cross-domain multimodal time series forecasting is a challenging task, requiring models to integrate precise numerical comprehension, cross-domain semantic understanding, and effective multimodal fusion. Existing approaches either build Time Series Foundation Models (TSFMs) from scratch or leverage pretrained Large Language Models (LLMs). However, TSFMs often overlook semantic understanding and lack the ability to perform future-oriented semantic reasoning, and LLMs struggle with numerical comprehension and accurate quantitative forecasting. To overcome these limitations, we propose KairosAgent, a novel agentic framework for multimodal time series forecasting, including an LLM-based reasoner and a TSFM-based forecaster. KairosAgent unifies textual reasoning and numerical forecasting by dynamically invoking analytical tools to enhance the numerical understanding and semantic reasoning capabilities of LLMs. The reasoning results are subsequently fused into the TSFM pipeline, enabling more accurate and reliable future predictions. To further improve the reasoning, we curate a large-scale corpus of high-quality trajectories, alongside a reinforcement learning from forecasting paradigm with multi-turn refinement and turn-level credit assignment. Experiments demonstrate that KairosAgent achieves superior zero-shot forecasting performance while maximizing the utility of pretrained LLMs and TSFMs, presenting a promising direction for efficient and interpretable time series agents. The project page is at https://foundation-model-research.github.io/KairosAgent .
AI Impact Assessments
(1 models)Scientific Impact Assessment: KairosAgent
1. Core Contribution
KairosAgent introduces an agentic framework that bridges two previously disconnected paradigms in time series forecasting: LLM-based semantic reasoning and Time Series Foundation Model (TSFM)-based numerical forecasting. The key insight is a reason-then-forecast pipeline where an LLM-based reasoner equipped with 23 statistical analysis tools produces a qualitative "morphology description" of expected future patterns, which is then fused as a semantic prior into a TSFM decoder via gated cross-attention.
Three specific contributions stand out: (1) the dynamic tool-calling mechanism that grounds LLM reasoning in quantitative evidence rather than naively serializing time series as text; (2) the T-STAR corpus of 40k+ quality-filtered reasoning trajectories spanning 29 datasets and 9 domains; and (3) a turn-level credit assignment scheme for reinforcement learning that provides denser supervision than outcome-only rewards in multi-turn agentic settings.
2. Methodological Rigor
Architecture Design: The separation of semantic reasoning from numerical prediction is well-motivated and cleanly executed. The gated cross-modal fusion (Equation 3) with gates initialized near zero (σ≈0.1) is a principled approach that preserves pretrained TSFM capabilities while gradually incorporating semantic information. The horizon-decoupled prediction heads enable multi-horizon forecasting in a single pass.
Training Pipeline: The three-stage training (SFT → multimodal alignment → RL with turn-level credit) is systematic. The turn-level advantage computation (marginal improvement Δᵢ = Sᵢ - Sᵢ₋₁) is a meaningful contribution over sparse outcome-level rewards, and the ablation in Table 3 shows that outcome-level RL actually *degrades* performance relative to SFT alone, while turn-level RL consistently improves it.
Evaluation: The paper evaluates on two established benchmarks (Time-MMD and Time-IMM) with proper data contamination controls (excluding Health/ILINet domains due to training overlap). The comparison includes zero-shot TSFMs, full-shot multimodal models, and full-shot unimodal baselines. Seed stability experiments (Table 11) and inference efficiency analysis (Table 12) add credibility.
Concerns: The reasoning evaluation relies on LLM-as-a-judge (GPT-5.2), which introduces potential biases and is not independently validated. Only three Time-MMD domains are evaluated for reasoning accuracy, leaving questions about generalizability. The paper does not investigate failure modes or cases where morphology descriptions mislead the forecaster.
3. Potential Impact
Practical Applications: The framework addresses a genuine need in operational forecasting where both interpretability and accuracy matter—finance, energy, infrastructure, and healthcare. The explicit reasoning traces provide auditability that black-box TSFMs lack.
Methodological Influence: The idea of using LLMs as semantic prior generators rather than direct forecasters could influence broader multimodal fusion research. The turn-level credit assignment for multi-turn agentic RL is applicable beyond time series to any tool-augmented reasoning setting.
Dataset Contribution: T-STAR fills a gap as there are few large-scale, quality-controlled corpora of tool-augmented time series reasoning trajectories. This could enable further research on time series agents.
Scalability: The forecaster adds only 40.6M parameters over the base TSFM, with marginal latency overhead (0.021s). However, the LLM reasoning stage (multi-turn tool calls) introduces significant latency not measured in the efficiency analysis—a notable omission for real-time applications.
4. Timeliness & Relevance
This work arrives at a convergence point where both TSFMs and LLM-based agents are maturing rapidly. The paper correctly identifies three critical limitations of existing approaches—inappropriate numerical serialization, modality disconnect, and optimization difficulty—and addresses each with specific mechanisms. The integration of agentic reasoning with foundation model forecasting is a timely research direction, as evidenced by concurrent works like TimeART and Cast-R1, against which KairosAgent is favorably positioned (Table 1).
5. Strengths & Limitations
Strengths:
Limitations:
Overall Assessment
KairosAgent represents a well-engineered contribution to multimodal time series forecasting that addresses real limitations in existing approaches. The framework design is principled, the experiments are thorough within their scope, and the results are strong. The main novelty lies in the integration pattern—using LLM-generated qualitative descriptions as fusible semantic priors rather than direct numerical outputs—which is both practical and theoretically motivated. However, the single-backbone limitation, incomplete reasoning evaluation, and missing end-to-end latency analysis temper the impact claims.
Generated May 29, 2026
Comparison History (16)
KairosAgent addresses a broader and more impactful problem—cross-domain multimodal time series forecasting—with a novel agentic framework combining LLMs and TSFMs, reinforcement learning from forecasting, and demonstrated zero-shot performance gains. It has wider real-world applicability across many domains. Paper 1, while methodologically interesting in diagnosing harmful continuations in CoT training, addresses a narrower, more incremental concern in LLM fine-tuning data quality. Paper 2's framework-level contribution and cross-domain applicability give it greater potential for broad scientific impact.
Paper 2 likely has higher scientific impact due to broader applicability and cross-field relevance: multimodal time series forecasting spans finance, healthcare, climate, operations, and science. Its agentic fusion of LLM semantic reasoning with TSFM numerical forecasting, plus a curated trajectory corpus and an RL-for-forecasting training paradigm, suggests methodological depth and a reusable framework that could influence both forecasting and agent research. Paper 1 addresses an important, timely systems problem in LLM service discovery, but its impact is more specialized to agent registries/tool retrieval compared to the wide downstream reach of forecasting advances.
Paper 2 addresses a critical and broad issue in AI agent evaluation by exposing the 'Outcome-Process Gap' where task success hides dangerous process anomalies. By providing a large-scale benchmark and taxonomy for agent safety and reliability, it offers foundational infrastructure that will broadly impact the rapidly growing field of autonomous agents. While Paper 1 presents an innovative multimodal approach, its impact is more narrowly confined to the time series forecasting domain.
KairosAgent addresses the fundamental challenge of multimodal time series forecasting across domains by combining LLMs and TSFMs in a novel agentic framework with reinforcement learning from forecasting. Its broader applicability across multiple domains (finance, weather, energy, etc.), novel fusion of semantic reasoning with numerical forecasting, and the introduction of RL-based training paradigm for time series agents represent a more impactful contribution. Paper 2, while valuable for interactive ASR, addresses a more niche problem with incremental improvements to an established pipeline. Paper 1's cross-domain generality and methodological innovations give it higher potential impact.
Paper 1 introduces a benchmark for LLM agent reflection and self-evolution, addressing fundamental bottlenecks in a rapidly growing field. Benchmarks that expose critical limitations and propose new metrics typically drive widespread future research across the broader AI community. In contrast, Paper 2 proposes a framework for a more specific application (time series forecasting), making its potential impact narrower.
Paper 2 addresses a fundamental and broadly applicable aspect of LLM training—data organization—that impacts virtually all LLM development. It provides systematic, generalizable guidelines (Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, Local Diversity) with minimal computational overhead, making it immediately practical. The work spans both pre-training and SFT stages across multiple scales, demonstrating broad applicability. Paper 1, while innovative in combining LLMs with TSFMs for time series forecasting, targets a narrower domain. Paper 2's insights are more foundational and likely to influence a wider range of future research and practice.
Paper 2 likely has higher impact due to broader applicability and timeliness: agentic multimodal time-series forecasting spans many real-world domains (finance, energy, healthcare, supply chains) and connects LLM reasoning, tool use, RL-style refinement, and TSFMs—an active, fast-moving area. Its contribution includes a new framework plus a curated trajectory corpus and a training paradigm (multi-turn refinement with credit assignment), which can seed follow-on work. Paper 1 is solid and practical but more narrowly scoped to post-training MoE compression and is incremental within model deployment optimization.
Paper 1 introduces a more broadly applicable conceptual shift (Memory-as-Cognition) for conversational agents, addressing fundamental limitations of one-shot retrieval with navigable memory graphs, multi-step navigation, and proactive triggering, plus a new benchmark (ProactiveMemBench). This combination of architectural reframing + evaluation infrastructure is likely to influence a wide range of agent, RAG, and long-context research. Paper 2 is timely and promising for multimodal forecasting, but its impact is narrower (time series) and depends more on integration/engineering and curated RL trajectories than on a generally reusable paradigm change.
KairosAgent presents a novel technical framework combining LLMs and time series foundation models with reinforcement learning, addressing a fundamental challenge in cross-domain multimodal forecasting. Its methodological contributions—agentic architecture, fused semantic reasoning, and a new RL paradigm—have broad applicability across many domains. Paper 1, while valuable as an ethnographic study of AI in music production, addresses a narrower audience and offers primarily descriptive findings about user sentiments rather than generalizable technical advances.
Paper 2 introduces a foundational benchmark for robotic world models, addressing a critical gap by shifting evaluation from visual fidelity to action-conditioned reliability and physical plausibility. Benchmarks that define how a community measures progress often have a broader, more enduring impact across multiple subfields than specific methodological frameworks like the time series forecasting agent proposed in Paper 1.
Paper 2 (KairosAgent) likely has higher scientific impact due to broader real-world applicability (cross-domain time-series forecasting across finance, energy, healthcare), timeliness (agentic tool-using LLMs + foundation models), and a clear systems innovation combining LLM reasoning with TSFM forecasting plus RL-based multi-turn refinement. If validated rigorously, it can influence both forecasting and agentic AI research. Paper 1 is valuable and novel for VLA interpretability/diagnostics, but its impact is narrower (primarily embodied AI debugging) and more analytical than enabling new capabilities.
KairosAgent addresses the fundamental challenge of multimodal time series forecasting with a concrete, well-developed framework combining LLMs and TSFMs. It introduces novel technical contributions including reinforcement learning from forecasting, multi-turn refinement, and turn-level credit assignment, backed by experimental validation of zero-shot performance. Paper 2 proposes a conceptually interesting but more incremental multi-agent medical AI framework. While both papers combine specialist and generalist models, Paper 1 offers broader cross-domain applicability, more technical depth, and a more novel methodological pipeline with greater potential to influence the rapidly growing time series foundation model field.
Paper 1 has higher likely scientific impact due to its novelty and timeliness for AI safety: it isolates alignment faking in a controlled setup, demonstrates broader prevalence (including small models), and identifies separable causal drivers validated via ablations and activation steering—supporting mechanistic understanding and actionable mitigation/detection. Its findings are broadly relevant across alignment, interpretability, evals, and deployment governance. Paper 2 targets an important application area, but the agentic-LLM+TSFM framework and RL trajectory refinement are more incremental within a crowded forecasting/agent literature, and impact depends heavily on dataset/benchmark adoption.
KairosAgent addresses the fundamental challenge of multimodal time series forecasting with a novel agentic framework combining LLMs and TSFMs, introducing reinforcement learning from forecasting and multi-turn refinement. It has broader real-world applicability across domains (finance, healthcare, energy) and proposes a generalizable paradigm. While EgoBench is a solid benchmark contribution revealing agent limitations, benchmarks typically have narrower impact than novel methodological frameworks. KairosAgent's fusion of semantic reasoning with numerical forecasting represents a more innovative architectural contribution with wider potential adoption.
Paper 1 proposes a novel methodological advancement in AI by integrating LLMs and Time Series Foundation Models for multimodal forecasting, which has broad applicability across various domains. Paper 2 is primarily a descriptive bibliometric study of trends in clinical trials. The technical innovation, rigorous methodology (including RL and tool use), and general-purpose nature of Paper 1 indicate a much higher potential for widespread scientific and real-world impact compared to the exploratory trend analysis of Paper 2.
KairosAgent addresses the broadly impactful problem of cross-domain multimodal time series forecasting by combining LLMs with time series foundation models in a novel agentic framework. It introduces reinforcement learning from forecasting, multi-turn refinement, and a large-scale trajectory corpus, with demonstrated zero-shot performance gains. Its breadth of applications (finance, weather, healthcare, etc.), methodological novelty in fusing semantic reasoning with numerical forecasting, and practical utility give it higher potential impact. Paper 2 addresses a narrower diagnostic problem (intra-policy rule conflicts in LLM agents) with a useful but more specialized pipeline that serves primarily as an analysis tool rather than enabling new capabilities.