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on Human-AI Collaboration","body":"title study human ai collaboration tags paper emoji modified ai ai ai role artificial intelligence human collaboration management global perspective ai ai ai ai hrm ai ai ai beyond ai powered context aware services role human ai collaboration ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai human ai collaboration context aware systems systematic review ai ai ai ai ai ai ai https //osf io/vfmkr/ https //www emerald com/insight/content/doi/ /imds /full/html https //www dbpia co kr/journal/articledetail nodeid node ai ai when combinations humans ai useful systematic review meta analysis ai ai ai ai nature human behaviour evaluating human ai collaboration review methodological framework ai haic haic ai ai arxiv cybernetic teammate field experiment generative ai reshaping teamwork expertise ai ai ai ai ai harvard business school ai ai ai ai dbpia ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai bias ai ai ai ai ai ai ai ai when combinations humans ai useful systematic review meta analysis ai ai ai identifying ethical issues ai partners human ai co creation ai ai ai ethics artificial intelligence case studies options addressing ethical challenges ai ai ai ai ai ai https //www geeksforgeeks org/top ethical issues artificial intelligence/ https //www frontiersin org/journals/psychology/articles/ /fpsyg /full https //arxiv org/abs/ https //koreascience kr/article/jako page https //www dbpia co kr/journal/articledetail nodeid node https //www dbpia co kr/journal/articledetail nodeid node https //www dbpia co kr/journal/articledetail nodeid node https //link springer com/book/ / https //acadexpinnara com/index php/jit/article/view/","tags":["paper"],"emoji":""},"2cbfc7aa-a469-5c03-8e62-b6fae2fc170a":{"id":"2cbfc7aa-a469-5c03-8e62-b6fae2fc170a","slug":"/paper-study-haic-2505282230","title":"인간-AI 협업 연구 트렌드 2025; 증강지능 시대의 새로운 패러다임","body":"title ai tags paper emoji modified ai ai ai augmented intelligence human loop ai ai ai ai ai ai human loop hitl hitl ai ai ai crew crew ai crew ai crew ai ai crew ai mixed initiative bayes pomcp ai midjourney dall e ai ai erica colorbo ai ai ibm watson lunit x ray aidoc ai ai ai ai ai ai ai ai ai arxiv explainable ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai ai crew ai ai sources ai crew https //www kosac re kr/menus/ /boards/ /posts/ brdtype r thispage bbidx brdcodevalue searchfield title searchtext page ai crew https //www kosac re kr/menus/ /boards/ /posts/ spid menuid com kcube jsv state spid cmenuid ai https //ai neon tistory com/ hyper h https //www newshyu com/news/articleview html idxno ai iot https //www gttkorea com/news/articleview html idxno ai appen https //kr appen com/blog/human centered ai/ https //ko wikipedia org/wiki/ ed ec d b eb b c eb ac eb c ec d b ed eb ac ec ed b ec b c ec ed c human loop appen https //kr appen com/blog/human loop/ mixed initiative human robot teaming https //www themoonlight io/ko/review/mixed initiative human 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node ai ai k smartfactory http //www k smartfactory org/index php/information/ai/board view message id https //www giikorea co kr/report/infi global augmented intelligence market html ai human loop https //www samsungsds com/kr/insights/human loop html ai https //www wiseai co kr/board/news view html idx page pdf ai lionbridge https //www lionbridge com/content/dam/lionbridge/pages/blogs/translation localization/human loop ai translations synergy people mach/why need human loop ai translations solution brief korean pdf ai ml hitl google cloud https //cloud google com/discover/human loop ai ai xai samsung sds https //www samsungsds com/kr/insights/explainable ai html","tags":["paper"],"emoji":""},"e9efa302-f99c-5474-bca6-0621d3216b3c":{"id":"e9efa302-f99c-5474-bca6-0621d3216b3c","slug":"/paper-study-haic-2505142330","title":"When Are Combinations of Humans and AI Useful? - A Systematic Review and Meta-Analysis","body":"title when combinations humans ai useful systematic review meta analysis tags paper emoji modified when combinations humans ai useful systematic review meta analysis abstract background inspired increasing use ai augment humans researchers studied human ai systems involving different tasks systems populations motivation despite large body work lack broad conceptual understanding when combinations humans ai better either alone methodology addressed question conducting pre registered systematic review meta analysis recent experimental studies reporting effect sizes results first found average human ai combinations performed significantly worse best humans ai alone hedges g confidence interval second found performance losses tasks involved making decisions significantly greater gains tasks involved creating content finally when humans outperformed ai alone found performance gains combination when ai outperformed humans alone found losses arguments findings highlight heterogeneity effects human ai collaboration point promising avenues improving human ai systems ai ai citation michelle vaccaro et al mit center collective intelligence sloan school management institute data systems society schwarzman college computing mit nature human behaviour received apr accepted sep published oct background ai ai ai ai ai ai methodology kitchenham systematic reviews meta analyses prisma preferred reporting items kitchenham systematic review planning conducting reporting meta analysis effect size prisma preferred reporting items systematic reviews meta analyses systematic review meta analysis literature review eligiblity criteria ai ai ai ai ai search strategy ai association computing machinery digital library acm dl association information systems elibrary aisel web science core collection wos ai human human expert participant humans experts participants ai ai artificial intelligence ml machine learning deep learning collaboration collaborate assist aid interact help experiment experiment experiments user study user studies crowdsourced study crowdsourced studies laboratory study laboratory studies coding we considered coded multiple potential moderators human ai performance namely publication date pre registration status experimental design data type task type task output ai type ai explanation ai confidence participant type performance metric data analysis calculation effect size we computed hedges g measure effect combining human artificial intelligence task performance hedges g cohen s d effect size meta analytic model effect size effect size random effects effect size rve effect size treatment control knapp hartung t p value u effect size k moderator moderator effect size effect size bias test human ai synergy publication bias viechtbauer cheung sensitivity analysis discussion performance loses human ai collaboration ai ai ai ai ai ai ai moderating effect task type ai ai moderating effect relative human/ai performance ai ai ai ai surprisingly insignificant moderators ai ai limitations roadmap future work finding human ai synergy ai ai ai human ai","tags":["paper"],"emoji":""},"a04abe85-1ea2-5e2d-bb77-ff8c8fff4697":{"id":"a04abe85-1ea2-5e2d-bb77-ff8c8fff4697","slug":"/paper-study-haic-2607222200","title":"2026년 5~7월 트레이딩 AI 에이전트 최신 연구 서베이","body":"title ai tags paper link https //sangkyuwernicke github io/report/survey emoji modified ai arxiv arxiv api confirmed workspace/ verification/verdicts md executive summary arxiv ai llm yao zheng beyond agent architecture https //arxiv org/abs/ ghosh devarakonda finbench https //arxiv org/abs/ zhu et al knowing doing https //arxiv org/abs/ confidence competence gap llm rl rl hjb halperin itkin sciphy rl https //arxiv org/abs/ lleo runggaldier rl risk sensitive investment management https //arxiv org/abs/ hmm rl verma et al regime based portfolio allocation https //arxiv org/abs/ llm coordination nechepurenko shuvalov coordination architectural layer https //arxiv org/abs/ shapley pei et al market regime council https //arxiv org/abs/ letteri agenticaita https 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portfolio yiqing wang et al https //arxiv org/abs/ hawkish/dovish llm debate etf etf llm knowing doing memory controlled benchmark llm trading agents taojie zhu et al https //arxiv org/abs/ ktd fin / llm / llm madevolve evolutionary optimization trading systems llms yurii kvasiuk et al https //arxiv org/abs/ llm bitcoin llm p hacking btc reflexivity prompt llm financial market forecasters eugene park https //arxiv org/abs/ reflexivity a/b augmenting fundamental analysis llms rag based investor briefs bartosz zi ko kacper dobrzeniecki https //arxiv org/abs/ rag llm sec b rl / b alphazerobeta deep rl market neutral portfolios boris belyakov https //arxiv org/abs/ cnn gru recurrent ppo alpha b sciphy rl portfolio optimization igor halperin andrey itkin https //arxiv org/abs/ rl hamilton jacobi bellman hjb distribution aware rl hjb b clqt closed loop benchmark llm portfolio management agents bo qu mingguang chen https //arxiv org/abs/ llm b rl risk sensitive investment management free energy entropy duality sebastien lleo wolfgang runggaldier https //arxiv org/abs/ rl b dynamic multi pair trading drl cryptocurrency damian lebied robert lepaczuk https //arxiv org/abs/ ppo lstm b recap regime adaptive continual learning portfolio management chaofan pan et al https //arxiv org/abs/ b regime based portfolio allocation hmm rl ajay kumar verma et al https //arxiv org/abs/ hmm rl hmm rl hmm rl rl hmm b drl diversified portfolio across global equity markets kamil kashif robert lepaczuk https //arxiv org/abs/ sac soft actor critic sac euro stoxx c c market regime council multi agent llm shapley credit assignment yunhua pei et al https //arxiv org/abs/ shapley llm shapley shapley c agenticaita deliberative multi agent reasoning autonomous trading ivan letteri https //arxiv org/abs/ deliberative reasoning loop poc c coordination architectural layer llm multi agent systems maksym nechepurenko pavel shuvalov https //arxiv org/abs/ llm d ai d tt dac ps twin target deterministic actor critic optimal trade execution ilia zaznov et al https //arxiv org/abs/ almgren chriss rl implementation shortfall d rl execution dynamic fees dex simulator wen ting wang https //arxiv org/abs/ amm dqn ppo dqn dex e / e vaiom decoder financial sequence modeling yiming ma xinyu chen https //arxiv org/abs/ bar lightgbm e learning predictive ambiguity sets dro portfolio junjie guo https //arxiv org/abs/ dro dro e cryptogat graph attention network crypto forecasting yu peng matloob khushi josiah poon https //arxiv org/abs/ gat e hybrid news sentiment engine market analysis andreas aigner https //arxiv org/abs/ tf idf cpu e multi modal dl stock price direction earnings days manuel noseda nathan soldati marco paina https //arxiv org/abs/ finbert transformer cnn lstm transformer e macro aware forecasting via hierarchical mixed frequency attention hanet daniel cunha oliveira et al https //arxiv org/abs/ f / f finbench time gated calibration agentic financial forecasting rishab ghosh vinay devarakonda https //arxiv org/abs/ llm confidence competence gap llm f point time financial rag market feedback zijie zhao roy e welsch https //arxiv org/abs/ llm pit rag f finstressts parametric synthetic benchmark ts forecasting jiaze sun et al https //arxiv org/abs/ transformer f lob prediction latency efficient architecture c evans hedges https //arxiv org/abs/ lob fastbinlob lob g g rl efficiently discover price manipulation ioanna yvonni tsaknaki andrea macr fabrizio lillo https //arxiv org/abs/ rl yao zheng https //arxiv org/abs/ llm ghosh devarakonda https //arxiv org/abs/ sun et al https //arxiv org/abs/ llm qu chen https //arxiv org/abs/ llm zhu et al https //arxiv org/abs/ ghosh devarakonda https //arxiv org/abs/ ntale https //arxiv org/abs/ llm llm zi ko dobrzeniecki https //arxiv org/abs/ rag llm wang et al https //arxiv org/abs/ pan et al https //arxiv org/abs/ hmm rl verma et al https //arxiv org/abs/ pei et al https //arxiv org/abs/ cunha oliveira et al https //arxiv org/abs/ nechepurenko shuvalov https //arxiv org/abs/ shapley pei et al https //arxiv org/abs/ poc letteri https //arxiv org/abs/ rl hjb halperin itkin https //arxiv org/abs/ lleo runggaldier https //arxiv org/abs/ dro guo https //arxiv org/abs/ zaznov et al https //arxiv org/abs/ amm wang https //arxiv org/abs/ lob hedges https //arxiv org/abs/ ma chen https //arxiv org/abs/ rl dual use tsaknaki et al https //arxiv org/abs/ kvasiuk et al https //arxiv org/abs/ p hacking confirmed yao zheng https //arxiv org/abs/ qu chen https //arxiv org/abs/ ghosh devarakonda https //arxiv org/abs/ neurips icml llm leakage zhu et al https //arxiv org/abs/ nechepurenko shuvalov https //arxiv org/abs/ llm dro halperin itkin https //arxiv org/abs/ lleo runggaldier https //arxiv org/abs/ guo https //arxiv org/abs/ tsaknaki et al https //arxiv org/abs/ kvasiuk et al https //arxiv org/abs/ p hacking ai llm zhu et al https //arxiv org/abs/ ghosh devarakonda https //arxiv org/abs/ rl halperin itkin https //arxiv org/abs/ lleo runggaldier https //arxiv org/abs/ nechepurenko shuvalov https //arxiv org/abs/ zaznov et al https //arxiv org/abs/ hedges https //arxiv org/abs/ arxiv range arxiv api arxiv id hypotheses factors constrained llm agents crypto sample oom rl market driven alignment llm multi agent systems machine spirits llm agents asset markets llm polyswarm multi agent llm prediction market trading llm finrl x ai native modular infrastructure quant trading alphaforgebench llm trading strategy design benchmark toward expert investment teams multi agent llm behavioral consistency validation llm agents rejected / unverified citation verifier rejected unverified arxiv api b confirmed llm geofrey ntale ai trading evaluating large language models technical market analysis arxiv https //arxiv org/abs/ junyi yao zihao zheng beyond agent architecture execution assumptions reproducibility llm based trading systems arxiv https //arxiv org/abs/ yiqing wang et al macro economists machine multi agent llm framework commodity etf portfolio arxiv https //arxiv org/abs/ taojie zhu et al knowing doing memory controlled benchmark llm trading agents arxiv https //arxiv org/abs/ yurii kvasiuk et al madevolve evolutionary optimization trading systems llms arxiv https //arxiv org/abs/ eugene park reflexivity prompt llm financial market forecasters arxiv https //arxiv org/abs/ bartosz zi ko kacper dobrzeniecki augmenting fundamental analysis llms rag based investor briefs arxiv https //arxiv org/abs/ b rl / boris belyakov alphazerobeta deep rl market neutral portfolios arxiv https //arxiv org/abs/ igor halperin andrey itkin sciphy reinforcement learning portfolio optimization arxiv https //arxiv org/abs/ bo qu mingguang chen clqt closed loop benchmark llm portfolio management agents arxiv https //arxiv org/abs/ sebastien lleo wolfgang runggaldier rl risk sensitive investment management free energy entropy duality approach arxiv https //arxiv org/abs/ damian lebied robert lepaczuk dynamic multi pair trading cryptocurrency via deep reinforcement learning arxiv https //arxiv org/abs/ chaofan pan et al recap regime adaptive continual learning portfolio management arxiv https //arxiv org/abs/ ajay kumar verma et al regime based portfolio allocation using hmm rl arxiv https //arxiv org/abs/ kamil kashif robert lepaczuk deep rl framework diversified portfolio management across global equity markets arxiv https //arxiv org/abs/ c yunhua pei et al market regime council dynamic credit assignment multi agent llm decision systems arxiv https //arxiv org/abs/ ivan letteri agenticaita poc deliberative multi agent reasoning autonomous trading systems arxiv https //arxiv org/abs/ maksym nechepurenko pavel shuvalov coordination architectural layer llm based multi agent systems arxiv https //arxiv org/abs/ d ai ilia zaznov et al tt dac ps twin target deterministic actor critic policy smoothing optimal trade execution arxiv https //arxiv org/abs/ wen ting wang rl execution dynamic fees closed loop dex simulator arxiv https //arxiv org/abs/ e / yiming ma xinyu chen vaiom continuous input discrete output decoder financial sequence modeling arxiv https //arxiv org/abs/ junjie guo learning predictive ambiguity sets decision focused dro arxiv https //arxiv org/abs/ yu peng matloob khushi josiah poon cryptogat time series models effective cryptocurrency forecasting arxiv https //arxiv org/abs/ andreas aigner hybrid news sentiment engine real time market analysis via adaptive ensemble learning news price pairs arxiv https //arxiv org/abs/ manuel noseda nathan soldati marco paina predicting stock price direction earnings announcement days using multi modal deep learning arxiv https //arxiv org/abs/ daniel cunha oliveira et al macro aware time series forecasting via hierarchical mixed frequency attention models arxiv https //arxiv org/abs/ f / rishab ghosh vinay devarakonda finbench time gated calibration uncertainty benchmarking agentic financial forecasting arxiv https //arxiv org/abs/ zijie zhao roy e welsch point time financial rag frozen llms market feedback adaptive retrieval arxiv https //arxiv org/abs/ jiaze sun et al finstressts parametric synthetic benchmark time series forecasting finance arxiv https //arxiv org/abs/ c evans hedges inference compute frontier latency efficient architecture limit order book prediction arxiv https //arxiv org/abs/ g ioanna yvonni tsaknaki andrea macr fabrizio lillo reinforcement learning efficiently discover price manipulation arxiv https //arxiv org/abs/ workspace/ verification/verdicts md workspace/ scout/candidates md workspace/ synthesis/notes md","tags":["paper"],"emoji":""},"e3c03f40-1d5f-5f07-9be3-d33509d4e2f0":{"id":"e3c03f40-1d5f-5f07-9be3-d33509d4e2f0","slug":"/paper-survey-haic-2607250949","title":"2026년 5~7월 트레이딩 AI 에이전트 최신 연구 동향","body":"title ai tags paper emoji modified ai arxiv arxiv api confirmed workspace/ verification/verdicts md executive summary arxiv ai llm yao zheng beyond agent architecture https //arxiv org/abs/ ghosh devarakonda finbench https //arxiv org/abs/ zhu et al knowing doing https //arxiv org/abs/ confidence competence gap llm rl rl hjb halperin itkin sciphy rl https //arxiv org/abs/ lleo runggaldier rl risk sensitive investment management https //arxiv org/abs/ hmm rl verma et al regime based portfolio allocation https //arxiv org/abs/ llm coordination nechepurenko shuvalov coordination architectural layer https //arxiv org/abs/ shapley pei et al market regime council https //arxiv org/abs/ letteri agenticaita https //arxiv org/abs/ tsaknaki et al rl efficiently discover price manipulation https //arxiv org/abs/ ai ai llm rl rl arxiv q fin cs lg cs ai cs ma llm trading deep reinforcement learning portfolio multi agent llm finance market regime limit order book execution optimal sentiment finance arxiv id yymm paper scout paper analyst citation verifier arxiv api survey synthesizer confirmed range rejected / unverified / arxiv api id list workspace/ verification/verdicts md arxiv arxiv neurips icml ssrn guo learning predictive ambiguity sets https //arxiv org/abs/ confirmed llm llm b rl / rl rl c d ai implementation shortfall amm e / f / pit rag lob g rl llm rl/ llm/rl llm ai trading evaluating llms technical market analysis geofrey ntale https //arxiv org/abs/ llm numeric hallucination beyond agent architecture execution assumptions reproducibility llm based trading systems junyi yao zihao zheng https //arxiv org/abs/ llm arxiv macro economists machine multi agent llm framework commodity etf portfolio yiqing wang et al https //arxiv org/abs/ hawkish/dovish llm debate etf etf llm knowing doing memory controlled benchmark llm trading agents taojie zhu et al https //arxiv org/abs/ ktd fin / llm / llm madevolve evolutionary optimization trading systems llms yurii kvasiuk et al https //arxiv org/abs/ llm bitcoin llm p hacking btc reflexivity prompt llm financial market forecasters eugene park https //arxiv org/abs/ reflexivity a/b augmenting fundamental analysis llms rag based investor briefs bartosz zi ko kacper dobrzeniecki https //arxiv org/abs/ rag llm sec b rl / b alphazerobeta deep rl market neutral portfolios boris belyakov https //arxiv org/abs/ cnn gru recurrent ppo alpha b sciphy rl portfolio optimization igor halperin andrey itkin https //arxiv org/abs/ rl hamilton jacobi bellman hjb distribution aware rl hjb b clqt closed loop benchmark llm portfolio management agents bo qu mingguang chen https //arxiv org/abs/ llm b rl risk sensitive investment management free energy entropy duality sebastien lleo wolfgang runggaldier https //arxiv org/abs/ rl b dynamic multi pair trading drl cryptocurrency damian lebied robert lepaczuk https //arxiv org/abs/ ppo lstm b recap regime adaptive continual learning portfolio management chaofan pan et al https //arxiv org/abs/ b regime based portfolio allocation hmm rl ajay kumar verma et al https //arxiv org/abs/ hmm rl hmm rl hmm rl rl hmm b drl diversified portfolio across global equity markets kamil kashif robert lepaczuk https //arxiv org/abs/ sac soft actor critic sac euro stoxx c c market regime council multi agent llm shapley credit assignment yunhua pei et al https //arxiv org/abs/ shapley llm shapley shapley c agenticaita deliberative multi agent reasoning autonomous trading ivan letteri https //arxiv org/abs/ deliberative reasoning loop poc c coordination architectural layer llm multi agent systems maksym nechepurenko pavel shuvalov https //arxiv org/abs/ llm d ai d tt dac ps twin target deterministic actor critic optimal trade execution ilia zaznov et al https //arxiv org/abs/ almgren chriss rl implementation shortfall d rl execution dynamic fees dex simulator wen ting wang https //arxiv org/abs/ amm dqn ppo dqn dex e / e vaiom decoder financial sequence modeling yiming ma xinyu chen https //arxiv org/abs/ bar lightgbm e learning predictive ambiguity sets dro portfolio junjie guo https //arxiv org/abs/ dro dro e cryptogat graph attention network crypto forecasting yu peng matloob khushi josiah poon https //arxiv org/abs/ gat e hybrid news sentiment engine market analysis andreas aigner https //arxiv org/abs/ tf idf cpu e multi modal dl stock price direction earnings days manuel noseda nathan soldati marco paina https //arxiv org/abs/ finbert transformer cnn lstm transformer e macro aware forecasting via hierarchical mixed frequency attention hanet daniel cunha oliveira et al https //arxiv org/abs/ f / f finbench time gated calibration agentic financial forecasting rishab ghosh vinay devarakonda https //arxiv org/abs/ llm confidence competence gap llm f point time financial rag market feedback zijie zhao roy e welsch https //arxiv org/abs/ llm pit rag f finstressts parametric synthetic benchmark ts forecasting jiaze sun et al https //arxiv org/abs/ transformer f lob prediction latency efficient architecture c evans hedges https //arxiv org/abs/ lob fastbinlob lob g g rl efficiently discover price manipulation ioanna yvonni tsaknaki andrea macr fabrizio lillo https //arxiv org/abs/ rl yao zheng https //arxiv org/abs/ llm ghosh devarakonda https //arxiv org/abs/ sun et al https //arxiv org/abs/ llm qu chen https //arxiv org/abs/ llm zhu et al https //arxiv org/abs/ ghosh devarakonda https //arxiv org/abs/ ntale https //arxiv org/abs/ llm llm zi ko dobrzeniecki https //arxiv org/abs/ rag llm wang et al https //arxiv org/abs/ pan et al https //arxiv org/abs/ hmm rl verma et al https //arxiv org/abs/ pei et al https //arxiv org/abs/ cunha oliveira et al https //arxiv org/abs/ nechepurenko shuvalov https //arxiv org/abs/ shapley pei et al https //arxiv org/abs/ poc letteri https //arxiv org/abs/ rl hjb halperin itkin https //arxiv org/abs/ lleo runggaldier https //arxiv org/abs/ dro guo https //arxiv org/abs/ zaznov et al https //arxiv org/abs/ amm wang https //arxiv org/abs/ lob hedges https //arxiv org/abs/ ma chen https //arxiv org/abs/ rl dual use tsaknaki et al https //arxiv org/abs/ kvasiuk et al https //arxiv org/abs/ p hacking confirmed yao zheng https //arxiv org/abs/ qu chen https //arxiv org/abs/ ghosh devarakonda https //arxiv org/abs/ neurips icml llm leakage zhu et al https //arxiv org/abs/ nechepurenko shuvalov https //arxiv org/abs/ llm dro halperin itkin https //arxiv org/abs/ lleo runggaldier https //arxiv org/abs/ guo https //arxiv org/abs/ tsaknaki et al https //arxiv org/abs/ kvasiuk et al https //arxiv org/abs/ p hacking ai llm zhu et al https //arxiv org/abs/ ghosh devarakonda https //arxiv org/abs/ rl halperin itkin https //arxiv org/abs/ lleo runggaldier https //arxiv org/abs/ nechepurenko shuvalov https //arxiv org/abs/ zaznov et al https //arxiv org/abs/ hedges https //arxiv org/abs/ arxiv range arxiv api arxiv id hypotheses factors constrained llm agents crypto sample oom rl market driven alignment llm multi agent systems machine spirits llm agents asset markets llm polyswarm multi agent llm prediction market trading llm finrl x ai native modular infrastructure quant trading alphaforgebench llm trading strategy design benchmark toward expert investment teams multi agent llm behavioral consistency validation llm agents rejected / unverified citation verifier rejected unverified arxiv api b confirmed llm geofrey ntale ai trading evaluating large language models technical market analysis arxiv https //arxiv org/abs/ junyi yao zihao zheng beyond agent architecture execution assumptions reproducibility llm based trading systems arxiv https //arxiv org/abs/ yiqing wang et al macro economists machine multi agent llm framework commodity etf portfolio arxiv https //arxiv org/abs/ taojie zhu et al knowing doing memory controlled benchmark llm trading agents arxiv https //arxiv org/abs/ yurii kvasiuk et al madevolve evolutionary optimization trading systems llms arxiv https //arxiv org/abs/ eugene park reflexivity prompt llm financial market forecasters arxiv https //arxiv org/abs/ bartosz zi ko kacper dobrzeniecki augmenting fundamental analysis llms rag based investor briefs arxiv https //arxiv org/abs/ b rl / boris belyakov alphazerobeta deep rl market neutral portfolios arxiv https //arxiv org/abs/ igor halperin andrey itkin sciphy reinforcement learning portfolio optimization arxiv https //arxiv org/abs/ bo qu mingguang chen clqt closed loop benchmark llm portfolio management agents arxiv https //arxiv org/abs/ sebastien lleo wolfgang runggaldier rl risk sensitive investment management free energy entropy duality approach arxiv https //arxiv org/abs/ damian lebied robert lepaczuk dynamic multi pair trading cryptocurrency via deep reinforcement learning arxiv https //arxiv org/abs/ chaofan pan et al recap regime adaptive continual learning portfolio management arxiv https //arxiv org/abs/ ajay kumar verma et al regime based portfolio allocation using hmm rl arxiv https //arxiv org/abs/ kamil kashif robert lepaczuk deep rl framework diversified portfolio management across global equity markets arxiv https //arxiv org/abs/ c yunhua pei et al market regime council dynamic credit assignment multi agent llm decision systems arxiv https //arxiv org/abs/ ivan letteri agenticaita poc deliberative multi agent reasoning autonomous trading systems arxiv https //arxiv org/abs/ maksym nechepurenko pavel shuvalov coordination architectural layer llm based multi agent systems arxiv https //arxiv org/abs/ d ai ilia zaznov et al tt dac ps twin target deterministic actor critic policy smoothing optimal trade execution arxiv https //arxiv org/abs/ wen ting wang rl execution dynamic fees closed loop dex simulator arxiv https //arxiv org/abs/ e / yiming ma xinyu chen vaiom continuous input discrete output decoder financial sequence modeling arxiv https //arxiv org/abs/ junjie guo learning predictive ambiguity sets decision focused dro arxiv https //arxiv org/abs/ yu peng matloob khushi josiah poon cryptogat time series models effective cryptocurrency forecasting arxiv https //arxiv org/abs/ andreas aigner hybrid news sentiment engine real time market analysis via adaptive ensemble learning news price pairs arxiv https //arxiv org/abs/ manuel noseda nathan soldati marco paina predicting stock price direction earnings announcement days using multi modal deep learning arxiv https //arxiv org/abs/ daniel cunha oliveira et al macro aware time series forecasting via hierarchical mixed frequency attention models arxiv https //arxiv org/abs/ f / rishab ghosh vinay devarakonda finbench time gated calibration uncertainty benchmarking agentic financial forecasting arxiv https //arxiv org/abs/ zijie zhao roy e welsch point time financial rag frozen llms market feedback adaptive retrieval arxiv https //arxiv org/abs/ jiaze sun et al finstressts parametric synthetic benchmark time series forecasting finance arxiv https //arxiv org/abs/ c evans hedges inference compute frontier latency efficient architecture limit order book prediction arxiv https //arxiv org/abs/ g ioanna yvonni tsaknaki andrea macr fabrizio lillo reinforcement learning efficiently discover price manipulation arxiv https //arxiv org/abs/ workspace/ verification/verdicts md workspace/ scout/candidates md workspace/ synthesis/notes md","tags":["paper"],"emoji":""},"97f2912f-374e-5ac7-8364-65d1950d188a":{"id":"97f2912f-374e-5ac7-8364-65d1950d188a","slug":"/paper-study-haic-2607250857","title":"Agentic RL Backtest & Bias Resolution Report","body":"title agentic rl backtest bias resolution report tags paper emoji modified agentic rl backtest bias resolution report mcp pytorch td llm finagent look ahead bias / mcp model context protocol pytorch td mcp sse port kospi agentic loop openai gpt o overriding rl td twin delayed deep deterministic policy gradient actor network critic network twin q net q q q value bias look ahead bias survival bias slot based dimension binding pytorch state action top b shares kospi clean liquidation tests/agentic rl backtest chart png slippage rebalancing drawdown rl reward function","tags":["paper"],"emoji":""},"13f35173-b764-514d-b534-6ac137e8c6f2":{"id":"13f35173-b764-514d-b534-6ac137e8c6f2","slug":"/example-note","title":"HelloWorld()","body":"title helloworld tags code emoji link https //sangkyuwernicke github io modified this sample code see below js resolve gatsby theme code notes options contentpath notes basepath / showthemeinfo true showdescriptioninsidebar true","tags":["code"],"emoji":"👋"},"5507970b-6320-5c53-9664-2180b771ea5e":{"id":"5507970b-6320-5c53-9664-2180b771ea5e","slug":"/paper-study-haic-2505142300","title":"ChatCollab; Exploring Collaboration Between Humans and AI Agents in Software Teams","body":"title chatcollab exploring collaboration humans ai agents software teams tags paper emoji modified chatcollab exploring collaboration humans ai agents software teams abstract ai ai / chatcollab ai slack ai / ai ai chatcollab ai chatcollab ai ai ceo ai ai chatcollab citation benjamin klieger et al stanford university arxiv v cs hc dec https //doi org/ /arxiv submitted dec background llms ai ai ai assisted software development github copilot tabnine replit ghostwriter codeium ai multi ai agent software development llm zelikman et al human interaction ai agents learning ai biswas et al ai ai ai ai ai ai ai chatcollab methodology ai chatcollab pasted image png pasted image png ai ai metagpt chatdev superagi chatcollab bales ipa interaction process analysis framework framework bales ipa llm shows solidarity shows tension release agrees gives suggestion gives opinion gives orientation asks orientation asks opinion asks suggestion disagrees shows tension shows antagonism collaborative configurability moderator ai chatcollab bales ipa llm ai pasted image png pasted image png discussion limitations limited collaboration analysis limited code quality analysis biases ai significant computational expenses ai agents don t know human dependence llms future work chatcollab 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